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null | null | transformers | @Piglin Talks Harry Potter | {"tags": ["conversational"]} | text-generation | CracklesCreeper/Piglin-Talks-Harry-Potter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| @Piglin Talks Harry Potter | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | sentence-transformers |
# A model.
| {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | feature-extraction | Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# A model.
| [
"# A model."
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# A model."
] | [
44,
4
] | [
"passage: TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n# A model."
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null | null | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a clone of the original model, with `pipeline_tag` metadata changed to `feature-extraction`, so it can just return the embedded vector. Otherwise it is unchanged.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L6-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | feature-extraction | Craig/paraphrase-MiniLM-L6-v2 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extraction', so it can just return the embedded vector. Otherwise it is unchanged.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extraction', so it can just return the embedded vector. Otherwise it is unchanged.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extraction', so it can just return the embedded vector. Otherwise it is unchanged.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
58,
111,
38,
64,
29,
5,
53
] | [
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extraction', so it can just return the embedded vector. Otherwise it is unchanged.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
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null | null | transformers |
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976
## Usage
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test")
tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test")
inputs = tokenizer("I am in love with you", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "en", "tags": "autonlp", "datasets": ["Crasher222/autonlp-data-kaggle-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 60.744727079482495} | text-classification | Crasher222/kaggle-comp-test | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976
## Usage
| [
"# Model Finetuned from BERT-base for\n\n- Problem type: Multi-class Classification\n- Model ID: 25805800",
"## Validation Metrics\n\n- Loss: 0.4422711133956909\n- Accuracy: 0.8615328555811976\n- Macro F1: 0.8642434650461513\n- Micro F1: 0.8615328555811976\n- Weighted F1: 0.8617743626671308\n- Macro Precision: 0.8649112225076049\n- Micro Precision: 0.8615328555811976\n- Weighted Precision: 0.8625407179375096\n- Macro Recall: 0.8640777539828228\n- Micro Recall: 0.8615328555811976\n- Weighted Recall: 0.8615328555811976",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Finetuned from BERT-base for\n\n- Problem type: Multi-class Classification\n- Model ID: 25805800",
"## Validation Metrics\n\n- Loss: 0.4422711133956909\n- Accuracy: 0.8615328555811976\n- Macro F1: 0.8642434650461513\n- Micro F1: 0.8615328555811976\n- Weighted F1: 0.8617743626671308\n- Macro Precision: 0.8649112225076049\n- Micro Precision: 0.8615328555811976\n- Weighted Precision: 0.8625407179375096\n- Macro Recall: 0.8640777539828228\n- Micro Recall: 0.8615328555811976\n- Weighted Recall: 0.8615328555811976",
"## Usage"
] | [
70,
27,
153,
3
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Crasher222/autonlp-data-kaggle-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Finetuned from BERT-base for\n\n- Problem type: Multi-class Classification\n- Model ID: 25805800## Validation Metrics\n\n- Loss: 0.4422711133956909\n- Accuracy: 0.8615328555811976\n- Macro F1: 0.8642434650461513\n- Micro F1: 0.8615328555811976\n- Weighted F1: 0.8617743626671308\n- Macro Precision: 0.8649112225076049\n- Micro Precision: 0.8615328555811976\n- Weighted Precision: 0.8625407179375096\n- Macro Recall: 0.8640777539828228\n- Micro Recall: 0.8615328555811976\n- Weighted Recall: 0.8615328555811976## Usage"
] | [
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] |
null | null | transformers | hello
| {} | text-generation | CrisLeaf/generador-de-historias-de-tolkien | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Accuracy: 0.9215
- F1: 0.9216
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7814 | 1.0 | 250 | 0.3105 | 0.907 | 0.9046 |
| 0.2401 | 2.0 | 500 | 0.2175 | 0.9215 | 0.9216 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9215, "name": "Accuracy"}, {"type": "f1", "value": 0.9215538311282218, "name": "F1"}]}]}]} | text-classification | Crives/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Accuracy: 0.9215
* F1: 0.9216
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
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"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] | [
67,
98,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
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null | null | transformers | #rick DialoGPT Model | {"tags": ["conversational"]} | text-generation | Cryptikdw/DialoGPT-small-rick | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #rick DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# Paladin Danse DialoGPT Model | {"tags": ["conversational"]} | text-generation | Cthyllax/DialoGPT-medium-PaladinDanse | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Paladin Danse DialoGPT Model | [
"# Paladin Danse DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Paladin Danse DialoGPT Model"
] | [
51,
10
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Paladin Danse DialoGPT Model"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0807
- Precision: 0.8927
- Recall: 0.8632
- F1: 0.8777
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0544 | 1.0 | 2904 | 0.0774 | 0.8859 | 0.8490 | 0.8670 | 0.9833 |
| 0.0284 | 2.0 | 5808 | 0.0781 | 0.8709 | 0.8590 | 0.8649 | 0.9840 |
| 0.0166 | 3.0 | 8712 | 0.0807 | 0.8927 | 0.8632 | 0.8777 | 0.9850 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "type": "mim_gold_ner", "args": "mim-gold-ner"}, "metrics": [{"type": "precision", "value": 0.8927335640138409, "name": "Precision"}, {"type": "recall", "value": 0.8631855657784682, "name": "Recall"}, {"type": "f1", "value": 0.8777109531620194, "name": "F1"}, {"type": "accuracy", "value": 0.9849836396073506, "name": "Accuracy"}]}]}]} | token-classification | Culmenus/IceBERT-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0807
* Precision: 0.8927
* Recall: 0.8632
* F1: 0.8777
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] | [
71,
98,
4,
34
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"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0891
- Precision: 0.8804
- Recall: 0.8517
- F1: 0.8658
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0573 | 1.0 | 2904 | 0.1024 | 0.8608 | 0.8003 | 0.8295 | 0.9799 |
| 0.0307 | 2.0 | 5808 | 0.0899 | 0.8707 | 0.8380 | 0.8540 | 0.9825 |
| 0.0198 | 3.0 | 8712 | 0.0891 | 0.8804 | 0.8517 | 0.8658 | 0.9837 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "type": "mim_gold_ner", "args": "mim-gold-ner"}, "metrics": [{"type": "precision", "value": 0.8803619696791632, "name": "Precision"}, {"type": "recall", "value": 0.8517339397384878, "name": "Recall"}, {"type": "f1", "value": 0.8658113730929264, "name": "F1"}, {"type": "accuracy", "value": 0.9837103244207861, "name": "Accuracy"}]}]}]} | token-classification | Culmenus/XLMR-ENIS-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0891
* Precision: 0.8804
* Recall: 0.8517
* F1: 0.8658
* Accuracy: 0.9837
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] | [
74,
98,
4,
34
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
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null | null | transformers |
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [Infore_25h dataset](https://files.huylenguyen.com/25hours.zip) (Password: BroughtToYouByInfoRe)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "vi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "vi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 58.63 %
## Training
The Common Voice `train`, `validation`, and `Infore_25h` datasets were used for training
The script used for training can be found [here](https://drive.google.com/file/d/1AW9R8IlsapiSGh9n3aECf23t-zhk3wUh/view?usp=sharing)
=======================To here===============================>
Your model in then available under *huggingface.co/CuongLD/wav2vec2-large-xlsr-vietnamese* for everybody to use 🎉.
## How to evaluate my trained checkpoint
Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as
copying the evaluation code of your model card into a python script and running it. Make sure to note
the final result on the model card **both** under the YAML tags at the very top **and** below your evaluation code under "Test Results".
## Rules of training and evaluation
In this section, we will quickly go over what data is allowed to be used as training
data, what kind of data preprocessing is allowed be used, and how the model should be evaluated.
To make it very simple regarding the first point: **All data except the official common voice `test` data set can be used as training data**. For models trained in a language that is not included in Common Voice, the author of the model is responsible to
leave a reasonable amount of data for evaluation.
Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to
normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical
symbols and punctuation marks. A list of such symbols can *e.g.* be fonud [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that
would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark `'` since it
would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. So the golden rule here is to
not remove any characters that could change the meaning of a word into another word. This is not always obvious and should
be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "`-`" since it doesn't change the
meaninng of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning.
Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was
done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586).
## Tips and tricks
This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.
### How to combine multiple datasets into one
Check out [this](https://discuss.huggingface.co/t/how-to-combine-local-data-files-with-an-official-dataset/4685) post.
### How to effectively preprocess the data
### How to do efficiently load datasets with limited ram and hard drive space
Check out [this](https://discuss.huggingface.co/t/german-asr-fine-tuning-wav2vec2/4558/8?u=patrickvonplaten) post.
### How to do hyperparameter tuning
### How to preprocess and evaluate character based languages
## Further reading material
It is recommended that take some time to read up on how Wav2vec2 works in theory.
Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model.
**However**, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.
If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:
- [Facebook's Wav2Vec2 blog post](https://ai.facebook.com/blog/wav2vec-state-of-the-art-speech-recognition-through-self-supervision/)
- [Official Wav2Vec2 paper](https://arxiv.org/abs/2006.11477)
- [Official XLSR Wav2vec2 paper](https://arxiv.org/pdf/2006.13979.pdf)
- [Hugging Face Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)
- [How does CTC (Connectionist Temporal Classification) work](https://distill.pub/2017/ctc/)
It helps to have a good understanding of the following points:
- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.
- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...
- What part of the model needs to be fine-tuned? -> The pretrained model **does not** include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to **not** further fine-tune the feature extractor.
- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on **53** languages.
- What languages are considered to be similar by XLSR-Wav2Vec2? In the official [XLSR Wav2Vec2 paper](https://arxiv.org/pdf/2006.13979.pdf), the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).
## FAQ
- Can a participant fine-tune models for more than one language?
Yes! A participant can fine-tune models in as many languages she/he likes
- Can a participant use extra data (apart from the common voice data)?
Yes! All data except the official common voice `test data` can be used for training.
If a participant wants to train a model on a language that is not part of Common Voice (which
is very much encouraged!), the participant should make sure that some test data is held out to
make sure the model is not overfitting.
- Can we fine-tune for high-resource languages?
Yes! While we do not really recommend people to fine-tune models in English since there are
already so many fine-tuned speech recognition models in English. However, it is very much
appreciated if participants want to fine-tune models in other "high-resource" languages, such
as French, Spanish, or German. For such cases, one probably needs to train locally and apply
might have to apply tricks such as lazy data loading (check the ["Lazy data loading"](#how-to-do-lazy-data-loading) section for more details).
| {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice, infore_25h"], "metrics": ["wer"], "model-index": [{"name": "Cuong-Cong XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice vi", "type": "common_voice", "args": "vi"}, "metrics": [{"type": "wer", "value": 58.63, "name": "Test WER"}]}]}]} | automatic-speech-recognition | CuongLD/wav2vec2-large-xlsr-vietnamese | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"vi",
"arxiv:2006.11477",
"arxiv:2006.13979",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2006.11477",
"2006.13979"
] | [
"vi"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #arxiv-2006.11477 #arxiv-2006.13979 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
Test Result: 58.63 %
## Training
The Common Voice 'train', 'validation', and 'Infore_25h' datasets were used for training
The script used for training can be found here
=======================To here===============================>
Your model in then available under *URL for everybody to use .
## How to evaluate my trained checkpoint
Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as
copying the evaluation code of your model card into a python script and running it. Make sure to note
the final result on the model card both under the YAML tags at the very top and below your evaluation code under "Test Results".
## Rules of training and evaluation
In this section, we will quickly go over what data is allowed to be used as training
data, what kind of data preprocessing is allowed be used, and how the model should be evaluated.
To make it very simple regarding the first point: All data except the official common voice 'test' data set can be used as training data. For models trained in a language that is not included in Common Voice, the author of the model is responsible to
leave a reasonable amount of data for evaluation.
Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to
normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical
symbols and punctuation marks. A list of such symbols can *e.g.* be fonud here - however here we already must be careful. We should not remove a symbol that
would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark ''' since it
would change the meaning of the word '"it's"' to '"its"' which would then be incorrect. So the golden rule here is to
not remove any characters that could change the meaning of a word into another word. This is not always obvious and should
be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "'-'" since it doesn't change the
meaninng of a word to another one. *E.g.* "'fine-tuning'" would be changed to "'finetuning'" which has still the same meaning.
Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was
done, *e.g.* here.
## Tips and tricks
This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.
### How to combine multiple datasets into one
Check out this post.
### How to effectively preprocess the data
### How to do efficiently load datasets with limited ram and hard drive space
Check out this post.
### How to do hyperparameter tuning
### How to preprocess and evaluate character based languages
## Further reading material
It is recommended that take some time to read up on how Wav2vec2 works in theory.
Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model.
However, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.
If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:
- Facebook's Wav2Vec2 blog post
- Official Wav2Vec2 paper
- Official XLSR Wav2vec2 paper
- Hugging Face Blog
- How does CTC (Connectionist Temporal Classification) work
It helps to have a good understanding of the following points:
- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.
- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...
- What part of the model needs to be fine-tuned? -> The pretrained model does not include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to not further fine-tune the feature extractor.
- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on 53 languages.
- What languages are considered to be similar by XLSR-Wav2Vec2? In the official XLSR Wav2Vec2 paper, the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).
## FAQ
- Can a participant fine-tune models for more than one language?
Yes! A participant can fine-tune models in as many languages she/he likes
- Can a participant use extra data (apart from the common voice data)?
Yes! All data except the official common voice 'test data' can be used for training.
If a participant wants to train a model on a language that is not part of Common Voice (which
is very much encouraged!), the participant should make sure that some test data is held out to
make sure the model is not overfitting.
- Can we fine-tune for high-resource languages?
Yes! While we do not really recommend people to fine-tune models in English since there are
already so many fine-tuned speech recognition models in English. However, it is very much
appreciated if participants want to fine-tune models in other "high-resource" languages, such
as French, Spanish, or German. For such cases, one probably needs to train locally and apply
might have to apply tricks such as lazy data loading (check the "Lazy data loading" section for more details).
| [
"# Wav2Vec2-Large-XLSR-53-Vietnamese \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)\n \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice. \n\n\n\nTest Result: 58.63 %",
"## Training\n\nThe Common Voice 'train', 'validation', and 'Infore_25h' datasets were used for training \n\nThe script used for training can be found here \n\n=======================To here===============================>\n\nYour model in then available under *URL for everybody to use .",
"## How to evaluate my trained checkpoint\n\nHaving uploaded your model, you should now evaluate your model in a final step. This should be as simple as \ncopying the evaluation code of your model card into a python script and running it. Make sure to note \nthe final result on the model card both under the YAML tags at the very top and below your evaluation code under \"Test Results\".",
"## Rules of training and evaluation\n\nIn this section, we will quickly go over what data is allowed to be used as training \ndata, what kind of data preprocessing is allowed be used, and how the model should be evaluated.\n\nTo make it very simple regarding the first point: All data except the official common voice 'test' data set can be used as training data. For models trained in a language that is not included in Common Voice, the author of the model is responsible to \nleave a reasonable amount of data for evaluation.\n\nSecond, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to \nnormalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical \nsymbols and punctuation marks. A list of such symbols can *e.g.* be fonud here - however here we already must be careful. We should not remove a symbol that \nwould change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark ''' since it \nwould change the meaning of the word '\"it's\"' to '\"its\"' which would then be incorrect. So the golden rule here is to \nnot remove any characters that could change the meaning of a word into another word. This is not always obvious and should \nbe given some consideration. As another example, it is fine to remove the \"Hypen-minus\" sign \"'-'\" since it doesn't change the \nmeaninng of a word to another one. *E.g.* \"'fine-tuning'\" would be changed to \"'finetuning'\" which has still the same meaning.\n\nSince those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was \ndone, *e.g.* here.",
"## Tips and tricks\n\nThis section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.",
"### How to combine multiple datasets into one\n\nCheck out this post.",
"### How to effectively preprocess the data",
"### How to do efficiently load datasets with limited ram and hard drive space\n\nCheck out this post.",
"### How to do hyperparameter tuning",
"### How to preprocess and evaluate character based languages",
"## Further reading material\n\nIt is recommended that take some time to read up on how Wav2vec2 works in theory. \nGetting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. \n\nHowever, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.\n\nIf you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:\n\n- Facebook's Wav2Vec2 blog post\n- Official Wav2Vec2 paper\n- Official XLSR Wav2vec2 paper\n- Hugging Face Blog\n- How does CTC (Connectionist Temporal Classification) work\n\nIt helps to have a good understanding of the following points:\n\n- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.\n\n- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...\n\n- What part of the model needs to be fine-tuned? -> The pretrained model does not include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to not further fine-tune the feature extractor.\n\n- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on 53 languages. \n\n- What languages are considered to be similar by XLSR-Wav2Vec2? In the official XLSR Wav2Vec2 paper, the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).",
"## FAQ\n\n- Can a participant fine-tune models for more than one language? \nYes! A participant can fine-tune models in as many languages she/he likes\n- Can a participant use extra data (apart from the common voice data)?\nYes! All data except the official common voice 'test data' can be used for training.\nIf a participant wants to train a model on a language that is not part of Common Voice (which \nis very much encouraged!), the participant should make sure that some test data is held out to \nmake sure the model is not overfitting.\n- Can we fine-tune for high-resource languages? \nYes! While we do not really recommend people to fine-tune models in English since there are\nalready so many fine-tuned speech recognition models in English. However, it is very much \nappreciated if participants want to fine-tune models in other \"high-resource\" languages, such \nas French, Spanish, or German. For such cases, one probably needs to train locally and apply \nmight have to apply tricks such as lazy data loading (check the \"Lazy data loading\" section for more details)."
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #arxiv-2006.11477 #arxiv-2006.13979 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Vietnamese \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)\n \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice. \n\n\n\nTest Result: 58.63 %",
"## Training\n\nThe Common Voice 'train', 'validation', and 'Infore_25h' datasets were used for training \n\nThe script used for training can be found here \n\n=======================To here===============================>\n\nYour model in then available under *URL for everybody to use .",
"## How to evaluate my trained checkpoint\n\nHaving uploaded your model, you should now evaluate your model in a final step. This should be as simple as \ncopying the evaluation code of your model card into a python script and running it. Make sure to note \nthe final result on the model card both under the YAML tags at the very top and below your evaluation code under \"Test Results\".",
"## Rules of training and evaluation\n\nIn this section, we will quickly go over what data is allowed to be used as training \ndata, what kind of data preprocessing is allowed be used, and how the model should be evaluated.\n\nTo make it very simple regarding the first point: All data except the official common voice 'test' data set can be used as training data. For models trained in a language that is not included in Common Voice, the author of the model is responsible to \nleave a reasonable amount of data for evaluation.\n\nSecond, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to \nnormalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical \nsymbols and punctuation marks. A list of such symbols can *e.g.* be fonud here - however here we already must be careful. We should not remove a symbol that \nwould change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark ''' since it \nwould change the meaning of the word '\"it's\"' to '\"its\"' which would then be incorrect. So the golden rule here is to \nnot remove any characters that could change the meaning of a word into another word. This is not always obvious and should \nbe given some consideration. As another example, it is fine to remove the \"Hypen-minus\" sign \"'-'\" since it doesn't change the \nmeaninng of a word to another one. *E.g.* \"'fine-tuning'\" would be changed to \"'finetuning'\" which has still the same meaning.\n\nSince those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was \ndone, *e.g.* here.",
"## Tips and tricks\n\nThis section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.",
"### How to combine multiple datasets into one\n\nCheck out this post.",
"### How to effectively preprocess the data",
"### How to do efficiently load datasets with limited ram and hard drive space\n\nCheck out this post.",
"### How to do hyperparameter tuning",
"### How to preprocess and evaluate character based languages",
"## Further reading material\n\nIt is recommended that take some time to read up on how Wav2vec2 works in theory. \nGetting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. \n\nHowever, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.\n\nIf you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:\n\n- Facebook's Wav2Vec2 blog post\n- Official Wav2Vec2 paper\n- Official XLSR Wav2vec2 paper\n- Hugging Face Blog\n- How does CTC (Connectionist Temporal Classification) work\n\nIt helps to have a good understanding of the following points:\n\n- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.\n\n- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...\n\n- What part of the model needs to be fine-tuned? -> The pretrained model does not include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to not further fine-tune the feature extractor.\n\n- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on 53 languages. \n\n- What languages are considered to be similar by XLSR-Wav2Vec2? In the official XLSR Wav2Vec2 paper, the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).",
"## FAQ\n\n- Can a participant fine-tune models for more than one language? \nYes! A participant can fine-tune models in as many languages she/he likes\n- Can a participant use extra data (apart from the common voice data)?\nYes! All data except the official common voice 'test data' can be used for training.\nIf a participant wants to train a model on a language that is not part of Common Voice (which \nis very much encouraged!), the participant should make sure that some test data is held out to \nmake sure the model is not overfitting.\n- Can we fine-tune for high-resource languages? \nYes! While we do not really recommend people to fine-tune models in English since there are\nalready so many fine-tuned speech recognition models in English. However, it is very much \nappreciated if participants want to fine-tune models in other \"high-resource\" languages, such \nas French, Spanish, or German. For such cases, one probably needs to train locally and apply \nmight have to apply tricks such as lazy data loading (check the \"Lazy data loading\" section for more details)."
] | [
89,
84,
20,
29,
70,
83,
405,
32,
16,
9,
23,
10,
13,
504,
243
] | [
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #arxiv-2006.11477 #arxiv-2006.13979 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Vietnamese \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)\n \nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice. \n\n\n\nTest Result: 58.63 %## Training\n\nThe Common Voice 'train', 'validation', and 'Infore_25h' datasets were used for training \n\nThe script used for training can be found here \n\n=======================To here===============================>\n\nYour model in then available under *URL for everybody to use .## How to evaluate my trained checkpoint\n\nHaving uploaded your model, you should now evaluate your model in a final step. This should be as simple as \ncopying the evaluation code of your model card into a python script and running it. Make sure to note \nthe final result on the model card both under the YAML tags at the very top and below your evaluation code under \"Test Results\".",
"passage: ## Rules of training and evaluation\n\nIn this section, we will quickly go over what data is allowed to be used as training \ndata, what kind of data preprocessing is allowed be used, and how the model should be evaluated.\n\nTo make it very simple regarding the first point: All data except the official common voice 'test' data set can be used as training data. For models trained in a language that is not included in Common Voice, the author of the model is responsible to \nleave a reasonable amount of data for evaluation.\n\nSecond, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to \nnormalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical \nsymbols and punctuation marks. A list of such symbols can *e.g.* be fonud here - however here we already must be careful. We should not remove a symbol that \nwould change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark ''' since it \nwould change the meaning of the word '\"it's\"' to '\"its\"' which would then be incorrect. So the golden rule here is to \nnot remove any characters that could change the meaning of a word into another word. This is not always obvious and should \nbe given some consideration. As another example, it is fine to remove the \"Hypen-minus\" sign \"'-'\" since it doesn't change the \nmeaninng of a word to another one. *E.g.* \"'fine-tuning'\" would be changed to \"'finetuning'\" which has still the same meaning.\n\nSince those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was \ndone, *e.g.* here.## Tips and tricks\n\nThis section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.### How to combine multiple datasets into one\n\nCheck out this post.### How to effectively preprocess the data### How to do efficiently load datasets with limited ram and hard drive space\n\nCheck out this post.### How to do hyperparameter tuning### How to preprocess and evaluate character based languages"
] | [
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null | null | null |
# Sora DialoGPT Model | {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-medium-sora-two | [
"pytorch",
"conversational",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#pytorch #conversational #region-us
|
# Sora DialoGPT Model | [
"# Sora DialoGPT Model"
] | [
"TAGS\n#pytorch #conversational #region-us \n",
"# Sora DialoGPT Model"
] | [
14,
8
] | [
"passage: TAGS\n#pytorch #conversational #region-us \n# Sora DialoGPT Model"
] | [
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null | null | transformers |
# Sora DialoGPT Model
| {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-medium-sora | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
|
# Sora DialoGPT Model
| [
"# Sora DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n",
"# Sora DialoGPT Model"
] | [
55,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n# Sora DialoGPT Model"
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null | null | transformers |
# Sora DialoGPT Model | {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-small-sora | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sora DialoGPT Model | [
"# Sora DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sora DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Sora DialoGPT Model"
] | [
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null | null | transformers |
# Chandler Bot DialoGPT model | {"tags": ["conversational"]} | text-generation | CyberMuffin/DialoGPT-small-ChandlerBot | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Chandler Bot DialoGPT model | [
"# Chandler Bot DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chandler Bot DialoGPT model"
] | [
51,
9
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Chandler Bot DialoGPT model"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-discriminator-finetuned-cola
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6367
- Matthews Correlation: 0.6824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4139 | 1.0 | 535 | 0.4137 | 0.6381 |
| 0.2452 | 2.0 | 1070 | 0.4887 | 0.6504 |
| 0.17 | 3.0 | 1605 | 0.5335 | 0.6757 |
| 0.1135 | 4.0 | 2140 | 0.6367 | 0.6824 |
| 0.0817 | 5.0 | 2675 | 0.6742 | 0.6755 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "electra-base-discriminator-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.6824089073723449, "name": "Matthews Correlation"}]}]}]} | text-classification | D3xter1922/electra-base-discriminator-finetuned-cola | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-cola
=========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6367
* Matthews Correlation: 0.6824
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] | [
66,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
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null | null | transformers |
# Anakin Skywalker DialoGPT Model | {"tags": ["conversational"]} | text-generation | DARKVIP3R/DialoGPT-medium-Anakin | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Anakin Skywalker DialoGPT Model | [
"# Anakin Skywalker DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Anakin Skywalker DialoGPT Model"
] | [
51,
11
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Anakin Skywalker DialoGPT Model"
] | [
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null | null | transformers |
# bert-base-irish-cased-v1
[gaBERT](https://aclanthology.org/2022.lrec-1.511/) is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
## Model description
Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.
## Intended uses & limitations
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
```
@inproceedings{barry-etal-2022-gabert,
title = "ga{BERT} {---} an {I}rish Language Model",
author = "Barry, James and
Wagner, Joachim and
Cassidy, Lauren and
Cowap, Alan and
Lynn, Teresa and
Walsh, Abigail and
{\'O} Meachair, M{\'\i}che{\'a}l J. and
Foster, Jennifer",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.511",
pages = "4774--4788",
abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
}
```
| {"tags": ["generated_from_keras_callback"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}], "model-index": [{"name": "bert-base-irish-cased-v1", "results": []}]} | fill-mask | DCU-NLP/bert-base-irish-cased-v1 | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-irish-cased-v1
gaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
## Model description
Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.
## Intended uses & limitations
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
| [
"# bert-base-irish-cased-v1\n\ngaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.",
"## Model description\n\nEncoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.",
"## Intended uses & limitations\n\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Framework versions\n\n- Transformers 4.20.1\n- TensorFlow 2.9.1\n- Datasets 2.3.2\n- Tokenizers 0.12.1",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-irish-cased-v1\n\ngaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.",
"## Model description\n\nEncoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.",
"## Intended uses & limitations\n\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Framework versions\n\n- Transformers 4.20.1\n- TensorFlow 2.9.1\n- Datasets 2.3.2\n- Tokenizers 0.12.1",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
50,
55,
28,
78,
33,
32,
27
] | [
"passage: TAGS\n#transformers #pytorch #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-irish-cased-v1\n\ngaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.## Model description\n\nEncoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.## Intended uses & limitations\n\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32### Framework versions\n\n- Transformers 4.20.1\n- TensorFlow 2.9.1\n- Datasets 2.3.2\n- Tokenizers 0.12.1### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
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] |
null | null | transformers |
# gaELECTRA
[gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and bias
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
```
@inproceedings{barry-etal-2022-gabert,
title = "ga{BERT} {---} an {I}rish Language Model",
author = "Barry, James and
Wagner, Joachim and
Cassidy, Lauren and
Cowap, Alan and
Lynn, Teresa and
Walsh, Abigail and
{\'O} Meachair, M{\'\i}che{\'a}l J. and
Foster, Jennifer",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.511",
pages = "4774--4788",
abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
}
``` | {"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]} | null | DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"transformers",
"pytorch",
"electra",
"pretraining",
"irish",
"ga",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #electra #pretraining #irish #ga #license-apache-2.0 #endpoints_compatible #region-us
|
# gaELECTRA
gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and bias
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
| [
"# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.",
"### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #irish #ga #license-apache-2.0 #endpoints_compatible #region-us \n",
"# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.",
"### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
40,
79,
76,
27
] | [
"passage: TAGS\n#transformers #pytorch #electra #pretraining #irish #ga #license-apache-2.0 #endpoints_compatible #region-us \n# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
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null | null | transformers |
# gaELECTRA
[gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and bias
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
```
@inproceedings{barry-etal-2022-gabert,
title = "ga{BERT} {---} an {I}rish Language Model",
author = "Barry, James and
Wagner, Joachim and
Cassidy, Lauren and
Cowap, Alan and
Lynn, Teresa and
Walsh, Abigail and
{\'O} Meachair, M{\'\i}che{\'a}l J. and
Foster, Jennifer",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.511",
pages = "4774--4788",
abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
}
``` | {"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]} | fill-mask | DCU-NLP/electra-base-irish-cased-generator-v1 | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"irish",
"ga",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #electra #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# gaELECTRA
gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and bias
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
| [
"# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.",
"### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.",
"### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.",
"### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
] | [
54,
79,
76,
27
] | [
"passage: TAGS\n#transformers #pytorch #electra #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.### Limitations and bias\nSome data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.### BibTeX entry and citation info\nIf you use this model in your research, please consider citing our paper:"
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] |
null | null | transformers |
# Danish BERT (uncased) model
[BotXO.ai](https://www.botxo.ai/) developed this model. For data and training details see their [GitHub repository](https://github.com/botxo/nordic_bert).
The original model was trained in TensorFlow then I converted it to Pytorch using [transformers-cli](https://huggingface.co/transformers/converting_tensorflow_models.html?highlight=cli).
For TensorFlow version download here: https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1
## Architecture
```python
from transformers import AutoModelForPreTraining
model = AutoModelForPreTraining.from_pretrained("DJSammy/bert-base-danish-uncased_BotXO,ai")
params = list(model.named_parameters())
print('danish_bert_uncased_v2 has {:} different named parameters.\n'.format(len(params)))
print('==== Embedding Layer ====\n')
for p in params[0:5]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== First Transformer ====\n')
for p in params[5:21]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== Last Transformer ====\n')
for p in params[181:197]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== Output Layer ====\n')
for p in params[197:]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
# danish_bert_uncased_v2 has 206 different named parameters.
# ==== Embedding Layer ====
# bert.embeddings.word_embeddings.weight (32000, 768)
# bert.embeddings.position_embeddings.weight (512, 768)
# bert.embeddings.token_type_embeddings.weight (2, 768)
# bert.embeddings.LayerNorm.weight (768,)
# bert.embeddings.LayerNorm.bias (768,)
# ==== First Transformer ====
# bert.encoder.layer.0.attention.self.query.weight (768, 768)
# bert.encoder.layer.0.attention.self.query.bias (768,)
# bert.encoder.layer.0.attention.self.key.weight (768, 768)
# bert.encoder.layer.0.attention.self.key.bias (768,)
# bert.encoder.layer.0.attention.self.value.weight (768, 768)
# bert.encoder.layer.0.attention.self.value.bias (768,)
# bert.encoder.layer.0.attention.output.dense.weight (768, 768)
# bert.encoder.layer.0.attention.output.dense.bias (768,)
# bert.encoder.layer.0.attention.output.LayerNorm.weight (768,)
# bert.encoder.layer.0.attention.output.LayerNorm.bias (768,)
# bert.encoder.layer.0.intermediate.dense.weight (3072, 768)
# bert.encoder.layer.0.intermediate.dense.bias (3072,)
# bert.encoder.layer.0.output.dense.weight (768, 3072)
# bert.encoder.layer.0.output.dense.bias (768,)
# bert.encoder.layer.0.output.LayerNorm.weight (768,)
# bert.encoder.layer.0.output.LayerNorm.bias (768,)
# ==== Last Transformer ====
# bert.encoder.layer.11.attention.self.query.weight (768, 768)
# bert.encoder.layer.11.attention.self.query.bias (768,)
# bert.encoder.layer.11.attention.self.key.weight (768, 768)
# bert.encoder.layer.11.attention.self.key.bias (768,)
# bert.encoder.layer.11.attention.self.value.weight (768, 768)
# bert.encoder.layer.11.attention.self.value.bias (768,)
# bert.encoder.layer.11.attention.output.dense.weight (768, 768)
# bert.encoder.layer.11.attention.output.dense.bias (768,)
# bert.encoder.layer.11.attention.output.LayerNorm.weight (768,)
# bert.encoder.layer.11.attention.output.LayerNorm.bias (768,)
# bert.encoder.layer.11.intermediate.dense.weight (3072, 768)
# bert.encoder.layer.11.intermediate.dense.bias (3072,)
# bert.encoder.layer.11.output.dense.weight (768, 3072)
# bert.encoder.layer.11.output.dense.bias (768,)
# bert.encoder.layer.11.output.LayerNorm.weight (768,)
# bert.encoder.layer.11.output.LayerNorm.bias (768,)
# ==== Output Layer ====
# bert.pooler.dense.weight (768, 768)
# bert.pooler.dense.bias (768,)
# cls.predictions.bias (32000,)
# cls.predictions.transform.dense.weight (768, 768)
# cls.predictions.transform.dense.bias (768,)
# cls.predictions.transform.LayerNorm.weight (768,)
# cls.predictions.transform.LayerNorm.bias (768,)
# cls.seq_relationship.weight (2, 768)
# cls.seq_relationship.bias (2,)
```
## Example Pipeline
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='DJSammy/bert-base-danish-uncased_BotXO,ai')
unmasker('København er [MASK] i Danmark.')
# Copenhagen is the [MASK] of Denmark.
# =>
# [{'score': 0.788068950176239,
# 'sequence': '[CLS] københavn er hovedstad i danmark. [SEP]',
# 'token': 12610,
# 'token_str': 'hovedstad'},
# {'score': 0.07606703042984009,
# 'sequence': '[CLS] københavn er hovedstaden i danmark. [SEP]',
# 'token': 8108,
# 'token_str': 'hovedstaden'},
# {'score': 0.04299738258123398,
# 'sequence': '[CLS] københavn er metropol i danmark. [SEP]',
# 'token': 23305,
# 'token_str': 'metropol'},
# {'score': 0.008163209073245525,
# 'sequence': '[CLS] københavn er ikke i danmark. [SEP]',
# 'token': 89,
# 'token_str': 'ikke'},
# {'score': 0.006238455418497324,
# 'sequence': '[CLS] københavn er ogsa i danmark. [SEP]',
# 'token': 25253,
# 'token_str': 'ogsa'}]
```
| {"language": "da", "license": "cc-by-4.0", "tags": ["bert", "masked-lm"], "datasets": ["common_crawl", "wikipedia"], "pipeline_tag": "fill-mask", "widget": [{"text": "K\u00f8benhavn er [MASK] i Danmark."}]} | fill-mask | DJSammy/bert-base-danish-uncased_BotXO-ai | [
"transformers",
"pytorch",
"jax",
"bert",
"masked-lm",
"fill-mask",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Danish BERT (uncased) model
URL developed this model. For data and training details see their GitHub repository.
The original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.
For TensorFlow version download here: URL
## Architecture
## Example Pipeline
| [
"# Danish BERT (uncased) model \n\nURL developed this model. For data and training details see their GitHub repository. \n\nThe original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.\n\nFor TensorFlow version download here: URL",
"## Architecture",
"## Example Pipeline"
] | [
"TAGS\n#transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Danish BERT (uncased) model \n\nURL developed this model. For data and training details see their GitHub repository. \n\nThe original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.\n\nFor TensorFlow version download here: URL",
"## Architecture",
"## Example Pipeline"
] | [
61,
66,
3,
5
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us \n# Danish BERT (uncased) model \n\nURL developed this model. For data and training details see their GitHub repository. \n\nThe original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.\n\nFor TensorFlow version download here: URL## Architecture## Example Pipeline"
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null | null | transformers | ** Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. | {} | text-classification | DSI/human-directed-sentiment | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
36
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English.
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
Models used in this paper are on HuggingFace:
- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support
- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-topics
| {"language": ["multilingual", "nl", "fr", "en"], "tags": ["Tweets", "Sentiment analysis"], "widget": [{"text": "I really wish I could leave my house after midnight, this makes no sense!"}]} | text-classification | DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Tweets",
"Sentiment analysis",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09947"
] | [
"multilingual",
"nl",
"fr",
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us
|
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Blog post » · paper »
This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English.
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
!URL
Models used in this paper are on HuggingFace:
- URL
- URL
| [
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nThis model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. \n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nThis model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. \n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
] | [
67,
191
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nThis model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. \n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
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] |
null | null | transformers |
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
Models used in this paper are on HuggingFace:
- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support
- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-topics
| {"language": ["multilingual", "nl", "fr", "en"], "tags": ["Dutch", "French", "English", "Tweets", "Topic classification"], "widget": [{"text": "I really can't wait for this lockdown to be over and go back to waking up early."}]} | text-classification | DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09947"
] | [
"multilingual",
"nl",
"fr",
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us
|
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Blog post » · paper »
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
!URL
Models used in this paper are on HuggingFace:
- URL
- URL
| [
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
] | [
77,
149
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).\n\n!URL\n\n\nModels used in this paper are on HuggingFace: \n- URL \n- URL"
] | [
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] |
null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | this model |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
| {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]} | fill-mask | DTAI-KULeuven/robbertje-1-gb-bort | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch the repository for updates.
News
====
* February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal!
* July 2, 2021: Publicly released 4 RobBERTje models.
* May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation!
The models
==========
Results
=======
Intrinsic results
-----------------
We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
Extrinsic results
-----------------
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
111
] | [
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | this model |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
| {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]} | fill-mask | DTAI-KULeuven/robbertje-1-gb-merged | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch the repository for updates.
News
====
* February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal!
* July 2, 2021: Publicly released 4 RobBERTje models.
* May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation!
The models
==========
Results
=======
Intrinsic results
-----------------
We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
Extrinsic results
-----------------
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.
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] | [
76
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"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | this model |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
| {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]} | fill-mask | DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch the repository for updates.
News
====
* February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal!
* July 2, 2021: Publicly released 4 RobBERTje models.
* May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation!
The models
==========
Results
=======
Intrinsic results
-----------------
We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
Extrinsic results
-----------------
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
111
] | [
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | this model |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
| {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]} | fill-mask | DTAI-KULeuven/robbertje-1-gb-shuffled | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch the repository for updates.
News
====
* February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal!
* July 2, 2021: Publicly released 4 RobBERTje models.
* May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation!
The models
==========
Results
=======
Intrinsic results
-----------------
We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
Extrinsic results
-----------------
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
81
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-binary-emotion-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-binary-emotion-classification-base")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Der er et tr\u00e6 i haven."}]} | text-classification | alexandrainst/da-binary-emotion-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | [
"# Danish BERT for emotion detection\n\nThe BERT Emotion model detects whether a Danish text is emotional or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT for emotion detection\n\nThe BERT Emotion model detects whether a Danish text is emotional or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
54,
70,
34
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Danish BERT for emotion detection\n\nThe BERT Emotion model detects whether a Danish text is emotional or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
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null | null | transformers |
# Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
* Glæde/Sindsro
* Tillid/Accept
* Forventning/Interrese
* Overasket/Målløs
* Vrede/Irritation
* Foragt/Modvilje
* Sorg/trist
* Frygt/Bekymret
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
This model should be used after detecting whether the text contains emotion or not, using the binary [BERT Emotion model](https://huggingface.co/alexandrainst/da-binary-emotion-classification-base).
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-emotion-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-emotion-classification-base")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Jeg ejer en r\u00f8d bil og det er en god bil."}]} | text-classification | alexandrainst/da-emotion-classification-base | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
* Glæde/Sindsro
* Tillid/Accept
* Forventning/Interrese
* Overasket/Målløs
* Vrede/Irritation
* Foragt/Modvilje
* Sorg/trist
* Frygt/Bekymret
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
This model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | [
"# Danish BERT for emotion classification\n\nThe BERT Emotion model classifies a Danish text in one of the following class:\n* Glæde/Sindsro\n* Tillid/Accept\n* Forventning/Interrese\n* Overasket/Målløs\n* Vrede/Irritation\n* Foragt/Modvilje\n* Sorg/trist\n* Frygt/Bekymret\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nThis model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model.\n\nSee the DaNLP documentation for more details. \n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Danish BERT for emotion classification\n\nThe BERT Emotion model classifies a Danish text in one of the following class:\n* Glæde/Sindsro\n* Tillid/Accept\n* Forventning/Interrese\n* Overasket/Målløs\n* Vrede/Irritation\n* Foragt/Modvilje\n* Sorg/trist\n* Frygt/Bekymret\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nThis model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model.\n\nSee the DaNLP documentation for more details. \n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
53,
158,
34
] | [
"passage: TAGS\n#transformers #pytorch #tf #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Danish BERT for emotion classification\n\nThe BERT Emotion model classifies a Danish text in one of the following class:\n* Glæde/Sindsro\n* Tillid/Accept\n* Forventning/Interrese\n* Overasket/Målløs\n* Vrede/Irritation\n* Foragt/Modvilje\n* Sorg/trist\n* Frygt/Bekymret\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nThis model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model.\n\nSee the DaNLP documentation for more details. \n\nHere is how to use the model:## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
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null | null | transformers |
# Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
* `Særlig opmærksomhed` (special attention, e.g. threat)
* `Personangreb` (personal attack)
* `Sprogbrug` (offensive language)
* `Spam & indhold` (spam)
This model is intended to be used after the [BERT HateSpeech detection model](https://huggingface.co/alexandrainst/da-hatespeech-detection-base).
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-classification-base")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
* 'Særlig opmærksomhed' (special attention, e.g. threat)
* 'Personangreb' (personal attack)
* 'Sprogbrug' (offensive language)
* 'Spam & indhold' (spam)
This model is intended to be used after the BERT HateSpeech detection model.
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | [
"# Danish BERT for hate speech classification\n\nThe BERT HateSpeech model classifies offensive Danish text into 4 categories: \n * 'Særlig opmærksomhed' (special attention, e.g. threat)\n * 'Personangreb' (personal attack) \n * 'Sprogbrug' (offensive language)\n * 'Spam & indhold' (spam)\nThis model is intended to be used after the BERT HateSpeech detection model.\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT for hate speech classification\n\nThe BERT HateSpeech model classifies offensive Danish text into 4 categories: \n * 'Særlig opmærksomhed' (special attention, e.g. threat)\n * 'Personangreb' (personal attack) \n * 'Sprogbrug' (offensive language)\n * 'Spam & indhold' (spam)\nThis model is intended to be used after the BERT HateSpeech detection model.\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
54,
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"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Danish BERT for hate speech classification\n\nThe BERT HateSpeech model classifies offensive Danish text into 4 categories: \n * 'Særlig opmærksomhed' (special attention, e.g. threat)\n * 'Personangreb' (personal attack) \n * 'Sprogbrug' (offensive language)\n * 'Spam & indhold' (spam)\nThis model is intended to be used after the BERT HateSpeech detection model.\n\nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
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null | null | transformers |
# Danish BERT for hate speech (offensive language) detection
The BERT HateSpeech model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-base")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-detection-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for hate speech (offensive language) detection
The BERT HateSpeech model detects whether a Danish text is offensive or not.
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | [
"# Danish BERT for hate speech (offensive language) detection\n\nThe BERT HateSpeech model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT for hate speech (offensive language) detection\n\nThe BERT HateSpeech model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
54,
80,
34
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Danish BERT for hate speech (offensive language) detection\n\nThe BERT HateSpeech model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
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null | null | transformers |
# BERT fine-tuned for Named Entity Recognition in Danish
The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].
The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ner.html#bert) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForTokenClassification
model = BertForTokenClassification.from_pretrained("alexandrainst/da-ner-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-ner-base")
```
## Training Data
The model has been trained on the [DaNE](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane). | {"language": ["da"], "license": "apache-2.0", "datasets": ["dane"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]} | token-classification | alexandrainst/da-ner-base | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"da",
"dataset:dane",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT fine-tuned for Named Entity Recognition in Danish
The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].
The pretrained language model used for fine-tuning is the Danish BERT by BotXO.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training Data
The model has been trained on the DaNE. | [
"# BERT fine-tuned for Named Entity Recognition in Danish\n\nThe model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].\nThe pretrained language model used for fine-tuning is the Danish BERT by BotXO. \n\nSee the DaNLP documentation for more details.\n\nHere is how to use the model:",
"## Training Data \n\nThe model has been trained on the DaNE."
] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT fine-tuned for Named Entity Recognition in Danish\n\nThe model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].\nThe pretrained language model used for fine-tuning is the Danish BERT by BotXO. \n\nSee the DaNLP documentation for more details.\n\nHere is how to use the model:",
"## Training Data \n\nThe model has been trained on the DaNE."
] | [
55,
90,
14
] | [
"passage: TAGS\n#transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT fine-tuned for Named Entity Recognition in Danish\n\nThe model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].\nThe pretrained language model used for fine-tuning is the Danish BERT by BotXO. \n\nSee the DaNLP documentation for more details.\n\nHere is how to use the model:## Training Data \n\nThe model has been trained on the DaNE."
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null | null | transformers |
# Model Card for Danish BERT
Danish BERT Tone for sentiment polarity detection
# Model Details
## Model Description
The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
- **Developed by:** DaNLP
- **Shared by [Optional]:** Hugging Face
- **Model type:** Text Classification
- **Language(s) (NLP):** Danish (da)
- **License:** cc-by-sa-4.0
- **Related Models:** More information needed
- **Parent Model:** BERT
- **Resources for more information:**
- [GitHub Repo](https://github.com/certainlyio/nordic_bert)
- [Associated Documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone)
# Uses
## Direct Use
This model can be used for text classification
## Downstream Use [Optional]
More information needed.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
## Training Procedure
### Preprocessing
It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO.
### Speeds, Sizes, Times
More information needed.
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed.
### Factors
### Metrics
F1
## Results
More information needed.
# Model Examination
More information needed.
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** More information needed.
- **Hours used:** More information needed.
- **Cloud Provider:** More information needed.
- **Compute Region:** More information needed.
- **Carbon Emitted:** More information needed.
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed.
## Compute Infrastructure
More information needed.
### Hardware
More information needed.
### Software
More information needed.
# Citation
**BibTeX:**
More information needed.
**APA:**
More information needed.
# Glossary [optional]
More information needed.
# More Information [optional]
More information needed.
# Model Card Authors [optional]
DaNLP in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed.
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base")
```
</details> | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Det er super godt"}]} | text-classification | alexandrainst/da-sentiment-base | [
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"text-classification",
"da",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Danish BERT
Danish BERT Tone for sentiment polarity detection
# Model Details
## Model Description
The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
- Developed by: DaNLP
- Shared by [Optional]: Hugging Face
- Model type: Text Classification
- Language(s) (NLP): Danish (da)
- License: cc-by-sa-4.0
- Related Models: More information needed
- Parent Model: BERT
- Resources for more information:
- GitHub Repo
- Associated Documentation
# Uses
## Direct Use
This model can be used for text classification
## Downstream Use [Optional]
More information needed.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.
## Training Procedure
### Preprocessing
It has been finetuned on the pretrained Danish BERT model by BotXO.
### Speeds, Sizes, Times
More information needed.
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed.
### Factors
### Metrics
F1
## Results
More information needed.
# Model Examination
More information needed.
# Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed.
- Hours used: More information needed.
- Cloud Provider: More information needed.
- Compute Region: More information needed.
- Carbon Emitted: More information needed.
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed.
## Compute Infrastructure
More information needed.
### Hardware
More information needed.
### Software
More information needed.
BibTeX:
More information needed.
APA:
More information needed.
# Glossary [optional]
More information needed.
# More Information [optional]
More information needed.
# Model Card Authors [optional]
DaNLP in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed.
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
</details> | [
"# Model Card for Danish BERT\n Danish BERT Tone for sentiment polarity detection",
"# Model Details",
"## Model Description\n \nThe BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.\n \n- Developed by: DaNLP\n- Shared by [Optional]: Hugging Face\n- Model type: Text Classification\n- Language(s) (NLP): Danish (da)\n- License: cc-by-sa-4.0\n- Related Models: More information needed\n - Parent Model: BERT\n- Resources for more information: \n - GitHub Repo \n - Associated Documentation",
"# Uses",
"## Direct Use\n \nThis model can be used for text classification",
"## Downstream Use [Optional]\n \n \nMore information needed.",
"## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.",
"# Bias, Risks, and Limitations\n \n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.",
"## Recommendations\n \n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"# Training Details",
"## Training Data\n \nThe data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.",
"## Training Procedure",
"### Preprocessing\n \nIt has been finetuned on the pretrained Danish BERT model by BotXO.",
"### Speeds, Sizes, Times\nMore information needed.",
"# Evaluation",
"## Testing Data, Factors & Metrics",
"### Testing Data\n \nMore information needed.",
"### Factors",
"### Metrics\n \nF1",
"## Results \n \nMore information needed.",
"# Model Examination\n \nMore information needed.",
"# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed.\n- Hours used: More information needed.\n- Cloud Provider: More information needed.\n- Compute Region: More information needed.\n- Carbon Emitted: More information needed.",
"# Technical Specifications [optional]",
"## Model Architecture and Objective\n \nMore information needed.",
"## Compute Infrastructure\n \nMore information needed.",
"### Hardware\n \nMore information needed.",
"### Software\n \nMore information needed.\n \nBibTeX:\n \nMore information needed.\n \nAPA:\n \nMore information needed.",
"# Glossary [optional]\n \nMore information needed.",
"# More Information [optional]\n \nMore information needed.",
"# Model Card Authors [optional]\n \nDaNLP in collaboration with Ezi Ozoani and the Hugging Face team",
"# Model Card Contact\n \nMore information needed.",
"# How to Get Started with the Model\n \nUse the code below to get started with the model.\n<details>\n<summary> Click to expand </summary>\n\n\n</details>"
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"# Model Details",
"## Model Description\n \nThe BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.\n \n- Developed by: DaNLP\n- Shared by [Optional]: Hugging Face\n- Model type: Text Classification\n- Language(s) (NLP): Danish (da)\n- License: cc-by-sa-4.0\n- Related Models: More information needed\n - Parent Model: BERT\n- Resources for more information: \n - GitHub Repo \n - Associated Documentation",
"# Uses",
"## Direct Use\n \nThis model can be used for text classification",
"## Downstream Use [Optional]\n \n \nMore information needed.",
"## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.",
"# Bias, Risks, and Limitations\n \n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.",
"## Recommendations\n \n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"# Training Details",
"## Training Data\n \nThe data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.",
"## Training Procedure",
"### Preprocessing\n \nIt has been finetuned on the pretrained Danish BERT model by BotXO.",
"### Speeds, Sizes, Times\nMore information needed.",
"# Evaluation",
"## Testing Data, Factors & Metrics",
"### Testing Data\n \nMore information needed.",
"### Factors",
"### Metrics\n \nF1",
"## Results \n \nMore information needed.",
"# Model Examination\n \nMore information needed.",
"# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed.\n- Hours used: More information needed.\n- Cloud Provider: More information needed.\n- Compute Region: More information needed.\n- Carbon Emitted: More information needed.",
"# Technical Specifications [optional]",
"## Model Architecture and Objective\n \nMore information needed.",
"## Compute Infrastructure\n \nMore information needed.",
"### Hardware\n \nMore information needed.",
"### Software\n \nMore information needed.\n \nBibTeX:\n \nMore information needed.\n \nAPA:\n \nMore information needed.",
"# Glossary [optional]\n \nMore information needed.",
"# More Information [optional]\n \nMore information needed.",
"# Model Card Authors [optional]\n \nDaNLP in collaboration with Ezi Ozoani and the Hugging Face team",
"# Model Card Contact\n \nMore information needed.",
"# How to Get Started with the Model\n \nUse the code below to get started with the model.\n<details>\n<summary> Click to expand </summary>\n\n\n</details>"
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null | null | transformers |
# Danish BERT Tone for the detection of subjectivity/objectivity
The BERT Tone model detects whether a text (in Danish) is subjective or objective.
The model is based on the finetuning of the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-subjectivivity-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-subjectivivity-classification-base")
```
## Training data
The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets. | {"language": ["da"], "license": "apache-2.0", "datasets": ["DDSC/twitter-sent", "DDSC/europarl"], "widget": [{"text": "Jeg tror alligvel, det bliver godt"}]} | text-classification | alexandrainst/da-subjectivivity-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #dataset-DDSC/twitter-sent #dataset-DDSC/europarl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT Tone for the detection of subjectivity/objectivity
The BERT Tone model detects whether a text (in Danish) is subjective or objective.
The model is based on the finetuning of the pretrained Danish BERT model by BotXO.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets. | [
"# Danish BERT Tone for the detection of subjectivity/objectivity\n\nThe BERT Tone model detects whether a text (in Danish) is subjective or objective. \nThe model is based on the finetuning of the pretrained Danish BERT model by BotXO. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #dataset-DDSC/twitter-sent #dataset-DDSC/europarl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT Tone for the detection of subjectivity/objectivity\n\nThe BERT Tone model detects whether a text (in Danish) is subjective or objective. \nThe model is based on the finetuning of the pretrained Danish BERT model by BotXO. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets."
] | [
73,
79,
24
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #da #dataset-DDSC/twitter-sent #dataset-DDSC/europarl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Danish BERT Tone for the detection of subjectivity/objectivity\n\nThe BERT Tone model detects whether a text (in Danish) is subjective or objective. \nThe model is based on the finetuning of the pretrained Danish BERT model by BotXO. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:## Training data\n\nThe data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets."
] | [
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null | null | transformers |
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish Ælæctra](Maltehb/aelaectra-danish-electra-small-cased) model.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#electra) for more details.
Here is how to use the model:
```python
from transformers import ElectraTokenizer, ElectraForSequenceClassification
model = ElectraForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-small")
tokenizer = ElectraTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-small")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-detection-small | [
"transformers",
"pytorch",
"electra",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #electra #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on the pretrained Danish Ælæctra model.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | [
"# Danish ELECTRA for hate speech (offensive language) detection\n\nThe ELECTRA Offensive model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish Ælæctra model. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
"TAGS\n#transformers #pytorch #electra #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish ELECTRA for hate speech (offensive language) detection\n\nThe ELECTRA Offensive model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish Ælæctra model. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
47,
68,
34
] | [
"passage: TAGS\n#transformers #pytorch #electra #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Danish ELECTRA for hate speech (offensive language) detection\n\nThe ELECTRA Offensive model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish Ælæctra model. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:## Training data\n\nThe data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio."
] | [
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null | null | transformers |
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model used is the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base).
Here is how to use the model:
```python
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
model = XLMRobertaForSequenceClassification.from_pretrained("alexandrainst/da-ned-base")
tokenizer = XLMRobertaTokenizer.from_pretrained("alexandrainst/da-ned-base")
```
The tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context.
Here is an example:
```python
sentence = "Karen Blixen vendte tilbage til Danmark, hvor hun boede resten af sit liv på Rungstedlund, som hun arvede efter sin mor i 1939"
kg_context = "udmærkelser modtaget Kritikerprisen udmærkelser modtaget Tagea Brandts Rejselegat udmærkelser modtaget Ingenio et arti udmærkelser modtaget Holbergmedaljen udmærkelser modtaget De Gyldne Laurbær mor Ingeborg Dinesen ægtefælle Bror von Blixen-Finecke køn kvinde Commons-kategori Karen Blixen LCAuth no95003722 VIAF 90663542 VIAF 121643918 GND-identifikator 118637878 ISNI 0000 0001 2096 6265 ISNI 0000 0003 6863 4408 ISNI 0000 0001 1891 0457 fødested Rungstedlund fødested Rungsted dødssted Rungstedlund dødssted København statsborgerskab Danmark NDL-nummer 00433530 dødsdato +1962-09-07T00:00:00Z dødsdato +1962-01-01T00:00:00Z fødselsdato +1885-04-17T00:00:00Z fødselsdato +1885-01-01T00:00:00Z AUT NKC jn20000600905 AUT NKC jo2015880827 AUT NKC xx0196181 emnets hovedkategori Kategori:Karen Blixen tilfælde af menneske billede Karen Blixen cropped from larger original.jpg IMDb-identifikationsnummer nm0227598 Freebase-ID /m/04ymd8w BNF 118857710 beskæftigelse skribent beskæftigelse selvbiograf beskæftigelse novelleforfatter ..."
```
A KG context, for a specific entity, can be generated from its Wikidata page.
In the previous example, the KG context is a string representation of the Wikidata page of [Karen Blixen (QID=Q182804)](https://www.wikidata.org/wiki/Q182804).
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ned.html#xlmr) for more details about how to generate a KG context.
## Training Data
The model has been trained on the [DaNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#daned) and [DaWikiNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dawikined) datasets. | {"language": ["da"], "license": "apache-2.0"} | text-classification | alexandrainst/da-ned-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model used is the xlm-roberta-base.
Here is how to use the model:
The tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context.
Here is an example:
A KG context, for a specific entity, can be generated from its Wikidata page.
In the previous example, the KG context is a string representation of the Wikidata page of Karen Blixen (QID=Q182804).
See the DaNLP documentation for more details about how to generate a KG context.
## Training Data
The model has been trained on the DaNED and DaWikiNED datasets. | [
"# XLM-Roberta fine-tuned for Named Entity Disambiguation\n\nGiven a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). \nThe base language model used is the xlm-roberta-base. \n\nHere is how to use the model: \n\n\n\nThe tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context. \nHere is an example:\n\n\nA KG context, for a specific entity, can be generated from its Wikidata page. \nIn the previous example, the KG context is a string representation of the Wikidata page of Karen Blixen (QID=Q182804). \nSee the DaNLP documentation for more details about how to generate a KG context.",
"## Training Data \n\nThe model has been trained on the DaNED and DaWikiNED datasets."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-Roberta fine-tuned for Named Entity Disambiguation\n\nGiven a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). \nThe base language model used is the xlm-roberta-base. \n\nHere is how to use the model: \n\n\n\nThe tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context. \nHere is an example:\n\n\nA KG context, for a specific entity, can be generated from its Wikidata page. \nIn the previous example, the KG context is a string representation of the Wikidata page of Karen Blixen (QID=Q182804). \nSee the DaNLP documentation for more details about how to generate a KG context.",
"## Training Data \n\nThe model has been trained on the DaNED and DaWikiNED datasets."
] | [
58,
180,
23
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# XLM-Roberta fine-tuned for Named Entity Disambiguation\n\nGiven a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). \nThe base language model used is the xlm-roberta-base. \n\nHere is how to use the model: \n\n\n\nThe tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context. \nHere is an example:\n\n\nA KG context, for a specific entity, can be generated from its Wikidata page. \nIn the previous example, the KG context is a string representation of the Wikidata page of Karen Blixen (QID=Q182804). \nSee the DaNLP documentation for more details about how to generate a KG context.## Training Data \n\nThe model has been trained on the DaNED and DaWikiNED datasets."
] | [
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null | null | transformers |
#Saitama DialoGPT model | {"tags": ["conversational"]} | text-generation | Daivakai/DialoGPT-small-saitama | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Saitama DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# scientific-challenges-and-directions
We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows:
* **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.
* **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration.
* This model here is described in our paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751) (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results).
* Our dataset can be found [here](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset).
* Please cite our paper if you use our datasets or models in your project. See the [BibTeX](#citation).
* Feel free to [email us](#contact-us).
* Also, check out [our search engine](https://challenges.apps.allenai.org/), as an example application.
## Model description
This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [scientific-challenges-and-directions-dataset](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset), designed for multi-label text classification.
## Training and evaluation data
The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751)
## Example notebook
We include an example notebook that uses the model for inference in our [repo](https://github.com/Dan-La/scientific-challenges-and-directions). See `Inference_Notebook.ipynb`.
A training notebook is also included.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning rate: 2e-05
- train batch size: 8
- eval batch size: 4
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr scheduler type: linear
- lr scheduler warmup steps: 500
- num epochs: 30
### Training results
The achieves the following results on the test set:
- Precision Challenge: 0.768719
- Recall Challenge: 0.780405
- F1 Challenge: 0.774518
- Precision Direction: 0.758112
- Recall Direction: 0.774096
- F1 Direction: 0.766021
- Precision (micro avg. on both labels): 0.764894
- Recall (micro avg. on both labels): 0.778139
- F1 (micro avg. on both labels): 0.771459
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
## Citation
If using our dataset and models, please cite:
```
@misc{lahav2021search,
title={A Search Engine for Discovery of Scientific Challenges and Directions},
author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope},
year={2021},
eprint={2108.13751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contact us
Please don't hesitate to reach out.
**Email:** `lahav@mail.tau.ac.il`,`tomh@allenai.org`.
| {"language": ["en"], "tags": ["generated_from_trainer", "text-classification"], "datasets": ["DanL/scientific-challenges-and-directions-dataset"], "metrics": ["precision", "recall", "f1"], "widget": [{"text": "severe atypical cases of pneumonia emerged and quickly spread worldwide.", "example_title": "challenge"}, {"text": "we speculate that studying IL-6 will be beneficial.", "example_title": "direction"}, {"text": "in future studies, both PRRs should be tested as the cause for multiple deaths.", "example_title": "both"}, {"text": "IbMADS1-transformed potatoes exhibited tuber morphogenesis in the fibrous roots.", "example_title": "neither"}], "model-index": [{"name": "scientific-challenges-and-directions", "results": []}]} | text-classification | DanL/scientific-challenges-and-directions | [
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"text-classification",
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"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
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] | 2022-03-02T23:29:04+00:00 | [
"2108.13751"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-DanL/scientific-challenges-and-directions-dataset #arxiv-2108.13751 #autotrain_compatible #endpoints_compatible #region-us
|
# scientific-challenges-and-directions
We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows:
* Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.
* Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration.
* This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results).
* Our dataset can be found here.
* Please cite our paper if you use our datasets or models in your project. See the BibTeX.
* Feel free to email us.
* Also, check out our search engine, as an example application.
## Model description
This model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification.
## Training and evaluation data
The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper
## Example notebook
We include an example notebook that uses the model for inference in our repo. See 'Inference_Notebook.ipynb'.
A training notebook is also included.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning rate: 2e-05
- train batch size: 8
- eval batch size: 4
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr scheduler type: linear
- lr scheduler warmup steps: 500
- num epochs: 30
### Training results
The achieves the following results on the test set:
- Precision Challenge: 0.768719
- Recall Challenge: 0.780405
- F1 Challenge: 0.774518
- Precision Direction: 0.758112
- Recall Direction: 0.774096
- F1 Direction: 0.766021
- Precision (micro avg. on both labels): 0.764894
- Recall (micro avg. on both labels): 0.778139
- F1 (micro avg. on both labels): 0.771459
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
If using our dataset and models, please cite:
## Contact us
Please don't hesitate to reach out.
Email: 'lahav@URL','tomh@URL'.
| [
"# scientific-challenges-and-directions\n\nWe present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows:\n\n* Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.\n* Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. \n\n* This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results).\n* Our dataset can be found here.\n* Please cite our paper if you use our datasets or models in your project. See the BibTeX. \n* Feel free to email us. \n* Also, check out our search engine, as an example application.",
"## Model description\nThis model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification.",
"## Training and evaluation data\nThe scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper",
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"### Training results\nThe achieves the following results on the test set:\n- Precision Challenge: 0.768719 \n- Recall Challenge: 0.780405 \n- F1 Challenge: 0.774518\n- Precision Direction: 0.758112 \n- Recall Direction: 0.774096 \n- F1 Direction: 0.766021\n- Precision (micro avg. on both labels): 0.764894 \n- Recall (micro avg. on both labels): 0.778139 \n- F1 (micro avg. on both labels): 0.771459",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3\n\nIf using our dataset and models, please cite:",
"## Contact us\n\nPlease don't hesitate to reach out.\n\nEmail: 'lahav@URL','tomh@URL'."
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"# scientific-challenges-and-directions\n\nWe present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows:\n\n* Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.\n* Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. \n\n* This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results).\n* Our dataset can be found here.\n* Please cite our paper if you use our datasets or models in your project. See the BibTeX. \n* Feel free to email us. \n* Also, check out our search engine, as an example application.",
"## Model description\nThis model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification.",
"## Training and evaluation data\nThe scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper",
"## Example notebook\nWe include an example notebook that uses the model for inference in our repo. See 'Inference_Notebook.ipynb'.\nA training notebook is also included.",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning rate: 2e-05\n- train batch size: 8\n- eval batch size: 4\n- seed: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr scheduler type: linear\n- lr scheduler warmup steps: 500\n- num epochs: 30",
"### Training results\nThe achieves the following results on the test set:\n- Precision Challenge: 0.768719 \n- Recall Challenge: 0.780405 \n- F1 Challenge: 0.774518\n- Precision Direction: 0.758112 \n- Recall Direction: 0.774096 \n- F1 Direction: 0.766021\n- Precision (micro avg. on both labels): 0.764894 \n- Recall (micro avg. on both labels): 0.778139 \n- F1 (micro avg. on both labels): 0.771459",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3\n\nIf using our dataset and models, please cite:",
"## Contact us\n\nPlease don't hesitate to reach out.\n\nEmail: 'lahav@URL','tomh@URL'."
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] |
null | null | transformers | Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking
# CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
It is further fine-tuned Med-Marco Dataset. MacAvaney et.al in their [paper](https://arxiv.org/abs/2010.05987) titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
| {} | text-classification | Darkrider/covidbert_medmarco | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2010.05987"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-2010.05987 #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking
# CovidBERT-MedNLI
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.
It is further fine-tuned Med-Marco Dataset. MacAvaney URL in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.
Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow
Base model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'.
| [
"# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\n\nIt is further fine-tuned Med-Marco Dataset. MacAvaney URL in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2010.05987 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\n\nIt is further fine-tuned Med-Marco Dataset. MacAvaney URL in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
47,
263
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2010.05987 #autotrain_compatible #endpoints_compatible #region-us \n# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\n\nIt is further fine-tuned Med-Marco Dataset. MacAvaney URL in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
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] |
null | null | transformers | # CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
It is further fine-tuned on both MedNLI datasets available at Physionet.
[ACL-BIONLP 2019](https://physionet.org/content/mednli-bionlp19/1.0.1/)
[MedNLI from MIMIC](https://physionet.org/content/mednli/1.0.0/)
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
| {} | null | Darkrider/covidbert_mednli | [
"transformers",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| # CovidBERT-MedNLI
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.
It is further fine-tuned on both MedNLI datasets available at Physionet.
ACL-BIONLP 2019
MedNLI from MIMIC
Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow
Base model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'.
| [
"# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\nIt is further fine-tuned on both MedNLI datasets available at Physionet. \n\nACL-BIONLP 2019\n\nMedNLI from MIMIC\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
"TAGS\n#transformers #endpoints_compatible #region-us \n",
"# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\nIt is further fine-tuned on both MedNLI datasets available at Physionet. \n\nACL-BIONLP 2019\n\nMedNLI from MIMIC\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
17,
188
] | [
"passage: TAGS\n#transformers #endpoints_compatible #region-us \n# CovidBERT-MedNLI\n\nThis is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.\n\nThe model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.\nIt is further fine-tuned on both MedNLI datasets available at Physionet. \n\nACL-BIONLP 2019\n\nMedNLI from MIMIC\n\n\nParameter details for the original training on CORD-19 are available on DeepSet's MLFlow\n\nBase model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'."
] | [
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null | null | transformers |
# Marathi DistilBERT
## Model description
This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.
```
DISCLAIMER
This model has not been thoroughly tested and may contain biased opinions or inappropriate language. User discretion is advised
```
## Training data
The training data has been extracted from a variety of sources, mainly including:
1. Oscar Corpus
2. Marathi Newspapers
3. Marathi storybooks and articles
The data is cleaned by removing all languages other than Marathi, while preserving common punctuations
## Training procedure
The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.
## Example
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="DarshanDeshpande/marathi-distilbert",
tokenizer="DarshanDeshpande/marathi-distilbert",
)
fill_mask("हा खरोखर चांगला [MASK] आहे.")
```
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<h3>Authors </h3>
<h5>1. Darshan Deshpande: <a href="https://github.com/DarshanDeshpande">GitHub</a>, <a href="https://www.linkedin.com/in/darshan-deshpande/">LinkedIn</a><h5>
<h5>2. Harshavardhan Abichandani: <a href="https://github.com/Baras64">GitHub</a>, <a href="http://www.linkedin.com/in/harsh-abhi">LinkedIn</a><h5> | {"language": ["mr"], "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["Oscar Corpus, News, Stories"], "widget": [{"text": "\u0939\u093e \u0916\u0930\u094b\u0916\u0930 \u091a\u093e\u0902\u0917\u0932\u093e [MASK] \u0906\u0939\u0947."}]} | fill-mask | DarshanDeshpande/marathi-distilbert | [
"transformers",
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.01108"
] | [
"mr"
] | TAGS
#transformers #pytorch #tf #distilbert #fill-mask #mr #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Marathi DistilBERT
## Model description
This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.
## Training data
The training data has been extracted from a variety of sources, mainly including:
1. Oscar Corpus
2. Marathi Newspapers
3. Marathi storybooks and articles
The data is cleaned by removing all languages other than Marathi, while preserving common punctuations
## Training procedure
The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.
## Example
### BibTeX entry and citation info
<h3>Authors </h3>
<h5>1. Darshan Deshpande: <a href="URL <a href="URL
<h5>2. Harshavardhan Abichandani: <a href="URL <a href="http://URL | [
"# Marathi DistilBERT",
"## Model description\n\nThis model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.",
"## Training data\nThe training data has been extracted from a variety of sources, mainly including:\n1. Oscar Corpus\n2. Marathi Newspapers\n3. Marathi storybooks and articles\n \nThe data is cleaned by removing all languages other than Marathi, while preserving common punctuations",
"## Training procedure\nThe model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.",
"## Example",
"### BibTeX entry and citation info\n\n\n\n<h3>Authors </h3>\n<h5>1. Darshan Deshpande: <a href=\"URL <a href=\"URL\n\n<h5>2. Harshavardhan Abichandani: <a href=\"URL <a href=\"http://URL"
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #mr #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Marathi DistilBERT",
"## Model description\n\nThis model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.",
"## Training data\nThe training data has been extracted from a variety of sources, mainly including:\n1. Oscar Corpus\n2. Marathi Newspapers\n3. Marathi storybooks and articles\n \nThe data is cleaned by removing all languages other than Marathi, while preserving common punctuations",
"## Training procedure\nThe model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.",
"## Example",
"### BibTeX entry and citation info\n\n\n\n<h3>Authors </h3>\n<h5>1. Darshan Deshpande: <a href=\"URL <a href=\"URL\n\n<h5>2. Harshavardhan Abichandani: <a href=\"URL <a href=\"http://URL"
] | [
59,
6,
51,
56,
61,
3,
75
] | [
"passage: TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #mr #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Marathi DistilBERT## Model description\n\nThis model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.## Training data\nThe training data has been extracted from a variety of sources, mainly including:\n1. Oscar Corpus\n2. Marathi Newspapers\n3. Marathi storybooks and articles\n \nThe data is cleaned by removing all languages other than Marathi, while preserving common punctuations## Training procedure\nThe model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.## Example### BibTeX entry and citation info\n\n\n\n<h3>Authors </h3>\n<h5>1. Darshan Deshpande: <a href=\"URL <a href=\"URL\n\n<h5>2. Harshavardhan Abichandani: <a href=\"URL <a href=\"http://URL"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-retrained_ru_covid
This model is a fine-tuned version of [blinoff/roberta-base-russian-v0](https://huggingface.co/blinoff/roberta-base-russian-v0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-retrained_ru_covid", "results": []}]} | fill-mask | Daryaflp/roberta-retrained_ru_covid | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-retrained_ru_covid
This model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| [
"# roberta-retrained_ru_covid\n\nThis model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8518",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-retrained_ru_covid\n\nThis model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8518",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] | [
48,
62,
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4,
35
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"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n# roberta-retrained_ru_covid\n\nThis model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8518## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25### Training results### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
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null | null | transformers | TinyBERT: Distilling BERT for Natural Language Understanding
========
**This model is a copy of [this model repository](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) from Huawei Noah at the specific commit `34707a33cd59a94ecde241ac209bf35103691b43`.**
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
For more details about the techniques of TinyBERT, refer to our paper:
[TinyBERT: Distilling BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351)
Citation
========
If you find TinyBERT useful in your research, please cite the following paper:
```
@article{jiao2019tinybert,
title={Tinybert: Distilling bert for natural language understanding},
author={Jiao, Xiaoqi and Yin, Yichun and Shang, Lifeng and Jiang, Xin and Chen, Xiao and Li, Linlin and Wang, Fang and Liu, Qun},
journal={arXiv preprint arXiv:1909.10351},
year={2019}
}
```
| {} | null | DataikuNLP/TinyBERT_General_4L_312D | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1909.10351"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #region-us
| TinyBERT: Distilling BERT for Natural Language Understanding
========
This model is a copy of this model repository from Huawei Noah at the specific commit '34707a33cd59a94ecde241ac209bf35103691b43'.
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
For more details about the techniques of TinyBERT, refer to our paper:
TinyBERT: Distilling BERT for Natural Language Understanding
Citation
========
If you find TinyBERT useful in your research, please cite the following paper:
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #region-us \n"
] | [
34
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #region-us \n"
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] |
null | null | sentence-transformers |
# average_word_embeddings_glove.6B.300d
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d) from sentence-transformers at the specific commit `5d2b7d1c127036ae98b9d487eca4d48744edc709`.**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/average_word_embeddings_glove.6B.300d')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/average_word_embeddings_glove.6B.300d)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(400001, 300)
)
(1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/average_word_embeddings_glove.6B.300d | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# average_word_embeddings_glove.6B.300d
This model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# average_word_embeddings_glove.6B.300d\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# average_word_embeddings_glove.6B.300d\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
49,
109,
38,
29,
5,
53
] | [
"passage: TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# average_word_embeddings_glove.6B.300d\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
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null | null | transformers |
# CamemBERT: a Tasty French Language Model
**This model is a copy of [this model repository](https://huggingface.co/camembert-base) at the specific commit `482393b6198924f9da270b1aaf37d238aafca99b`.**
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
camembert = CamembertModel.from_pretrained("camembert-base")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base")
results = camembert_fill_mask("Le camembert est <mask> :)")
# results
#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},
# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183},
# {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202},
# {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528},
# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
# tensor([[[-0.0254, 0.0235, 0.1027, ..., -0.1459, -0.0205, -0.0116],
# [ 0.0606, -0.1811, -0.0418, ..., -0.1815, 0.0880, -0.0766],
# [-0.1561, -0.1127, 0.2687, ..., -0.0648, 0.0249, 0.0446],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert-base", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[-0.0032, 0.0075, 0.0040, ..., -0.0025, -0.0178, -0.0210],
# [-0.0996, -0.1474, 0.1057, ..., -0.0278, 0.1690, -0.2982],
# [ 0.0557, -0.0588, 0.0547, ..., -0.0726, -0.0867, 0.0699],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
| {"language": "fr", "license": "mit", "datasets": ["oscar"]} | fill-mask | DataikuNLP/camembert-base | [
"transformers",
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #camembert #fill-mask #fr #dataset-oscar #arxiv-1911.03894 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
This model is a copy of this model repository at the specific commit '482393b6198924f9da270b1aaf37d238aafca99b'.
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
| [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] | [
"TAGS\n#transformers #pytorch #tf #camembert #fill-mask #fr #dataset-oscar #arxiv-1911.03894 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] | [
63,
17,
9,
15,
91
] | [
"passage: TAGS\n#transformers #pytorch #tf #camembert #fill-mask #fr #dataset-oscar #arxiv-1911.03894 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
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] |
null | null | sentence-transformers |
# DataikuNLP/distiluse-base-multilingual-cased-v1
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) from sentence-transformers at the specific commit `3a706e4d65c04f868c4684adfd4da74141be8732`.**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v1)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# DataikuNLP/distiluse-base-multilingual-cased-v1
This model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# DataikuNLP/distiluse-base-multilingual-cased-v1\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DataikuNLP/distiluse-base-multilingual-cased-v1\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
60,
112,
38,
29,
5,
53
] | [
"passage: TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# DataikuNLP/distiluse-base-multilingual-cased-v1\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
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null | null | sentence-transformers |
# DataikuNLP/paraphrase-MiniLM-L6-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2/) from sentence-transformers at the specific commit `c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e`.**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L6-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-MiniLM-L6-v2 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# DataikuNLP/paraphrase-MiniLM-L6-v2
This model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# DataikuNLP/paraphrase-MiniLM-L6-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DataikuNLP/paraphrase-MiniLM-L6-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
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"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# DataikuNLP/paraphrase-MiniLM-L6-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
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] |
null | null | sentence-transformers |
# DataikuNLP/paraphrase-albert-small-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2/) from sentence-transformers at the specific commit `1eb1996223dd90a4c25be2fc52f6f336419a0d52`.**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-albert-small-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-albert-small-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-small-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-albert-small-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-albert-small-v2 | [
"sentence-transformers",
"pytorch",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# DataikuNLP/paraphrase-albert-small-v2
This model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# DataikuNLP/paraphrase-albert-small-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DataikuNLP/paraphrase-albert-small-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
59,
110,
38,
64,
29,
5,
53
] | [
"passage: TAGS\n#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# DataikuNLP/paraphrase-albert-small-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
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null | null | sentence-transformers |
# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) from sentence-transformers at the specific commit `d66eff4d8a8598f264f166af8db67f7797164651`.**
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
This model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
| [
"# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] | [
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"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
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null | null | transformers | Hugging Face's logo
---
language: am
datasets:
---
# bert-base-multilingual-cased-finetuned-amharic
## Model description
**bert-base-multilingual-cased-finetuned-amharic** is a **Amharic BERT** model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning **bert-base-multilingual-cased** model on Amharic language texts. It provides **better performance** than the multilingual Amharic on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Amharic corpus using Amharic vocabulary.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-amharic')
>>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን [MASK] መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | am_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 0.0 | 60.89
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language: am
datasets:
---
bert-base-multilingual-cased-finetuned-amharic
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Amharic corpus using Amharic vocabulary.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Amharic CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 0.0, am\_bert F1: 60.89
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 0.0, am\\_bert F1: 60.89",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 0.0, am\\_bert F1: 60.89",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
122,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 0.0, am\\_bert F1: 60.89### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-hausa
## Model description
**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-hausa')
>>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")
[{'sequence':
'[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]',
'score': 0.9762618541717529,
'token': 22045,
'token_str': 'Nigeria'},
{'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181,
'token': 25444,
'token_str': 'Ka'},
{'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194,
'token': 117,
'token_str': ','},
{'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017,
'token': 28682,
'token_str': 'Ghana'},
{'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171,
'token': 11717,
'token_str': '##mu'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | ha_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.65 | 91.31
[VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | 84.76 | 90.98
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
bert-base-multilingual-cased-finetuned-hausa
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-hausa is a Hausa BERT model obtained by fine-tuning bert-base-multilingual-cased model on Hausa language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Hausa CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 86.65, ha\_bert F1: 91.31
Dataset: VOA Hausa Textclass, mBERT F1: 84.76, ha\_bert F1: 90.98
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.65, ha\\_bert F1: 91.31\nDataset: VOA Hausa Textclass, mBERT F1: 84.76, ha\\_bert F1: 90.98",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.65, ha\\_bert F1: 91.31\nDataset: VOA Hausa Textclass, mBERT F1: 84.76, ha\\_bert F1: 90.98",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
42,
27,
149,
15
] | [
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.65, ha\\_bert F1: 91.31\nDataset: VOA Hausa Textclass, mBERT F1: 84.76, ha\\_bert F1: 90.98### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: ig
datasets:
---
# bert-base-multilingual-cased-finetuned-igbo
## Model description
**bert-base-multilingual-cased-finetuned-igbo** is a **Igbo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Igbo language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Igbo corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-igbo')
>>> unmasker("Reno Omokri na Gọọmentị [MASK] enweghị ihe ha ga-eji hiwe ya bụ mmachi.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | ig_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 85.11 | 86.75
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ig
datasets:
---
bert-base-multilingual-cased-finetuned-igbo
===========================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-igbo is a Igbo BERT model obtained by fine-tuning bert-base-multilingual-cased model on Igbo language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Igbo corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 85.11, ig\_bert F1: 86.75
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 85.11, ig\\_bert F1: 86.75",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 85.11, ig\\_bert F1: 86.75",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
41,
27,
144,
15
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 85.11, ig\\_bert F1: 86.75### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: rw
datasets:
---
# bert-base-multilingual-cased-finetuned-kinyarwanda
## Model description
**bert-base-multilingual-cased-finetuned-kinyarwanda** is a **Kinyarwanda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Kinyarwanda language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda')
>>> unmasker("Twabonye ko igihe mu [MASK] hazaba hari ikirango abantu bakunze")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | rw_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 72.20 | 77.57
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: rw
datasets:
---
bert-base-multilingual-cased-finetuned-kinyarwanda
==================================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-kinyarwanda is a Kinyarwanda BERT model obtained by fine-tuning bert-base-multilingual-cased model on Kinyarwanda language texts. It provides better performance than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 72.20, rw\_bert F1: 77.57
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 72.20, rw\\_bert F1: 77.57",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 72.20, rw\\_bert F1: 77.57",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
36,
27,
133,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 72.20, rw\\_bert F1: 77.57### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: lg
datasets:
---
# bert-base-multilingual-cased-finetuned-luganda
## Model description
**bert-base-multilingual-cased-finetuned-luganda** is a **Luganda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luganda language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luganda')
>>> unmasker("Ffe tulwanyisa abo abaagala okutabangula [MASK], Kimuli bwe yategeezezza.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | lg_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 80.36 | 84.70
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-luganda | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: lg
datasets:
---
bert-base-multilingual-cased-finetuned-luganda
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-luganda is a Luganda BERT model obtained by fine-tuning bert-base-multilingual-cased model on Luganda language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 80.36, lg\_bert F1: 84.70
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 80.36, lg\\_bert F1: 84.70",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 80.36, lg\\_bert F1: 84.70",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
36,
27,
134,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 80.36, lg\\_bert F1: 84.70### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
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null | null | transformers | Hugging Face's logo
---
language: luo
datasets:
---
# bert-base-multilingual-cased-finetuned-luo
## Model description
**bert-base-multilingual-cased-finetuned-luo** is a **Luo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luo language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luo')
>>> unmasker("Obila ma Changamwe [MASK] pedho achije angwen mag njore")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | luo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.22 | 75.59
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-luo | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: luo
datasets:
---
bert-base-multilingual-cased-finetuned-luo
==========================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-luo is a Luo BERT model obtained by fine-tuning bert-base-multilingual-cased model on Luo language texts. It provides better performance than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 74.22, luo\_bert F1: 75.59
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 74.22, luo\\_bert F1: 75.59",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 74.22, luo\\_bert F1: 75.59",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
36,
27,
123,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 74.22, luo\\_bert F1: 75.59### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: pcm
datasets:
---
# bert-base-multilingual-cased-finetuned-naija
## Model description
**bert-base-multilingual-cased-finetuned-naija** is a **Nigerian-Pidgin BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Nigerian-Pidgin language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Nigerian-Pidgin corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-naija')
>>> unmasker("Another attack on ambulance happen for Koforidua in March [MASK] year where robbers kill Ambulance driver")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | pcm_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.23 | 89.95
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-naija | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: pcm
datasets:
---
bert-base-multilingual-cased-finetuned-naija
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-naija is a Nigerian-Pidgin BERT model obtained by fine-tuning bert-base-multilingual-cased model on Nigerian-Pidgin language texts. It provides better performance than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Nigerian-Pidgin corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + BBC Pidgin
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 87.23, pcm\_bert F1: 89.95
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 87.23, pcm\\_bert F1: 89.95",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 87.23, pcm\\_bert F1: 89.95",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
36,
27,
129,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 87.23, pcm\\_bert F1: 89.95### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-swahili
## Model description
**bert-base-multilingual-cased-finetuned-swahili** is a **Swahili BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Swahili language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa")
[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.31642526388168335,
'token': 10728,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa',
'score': 0.15753623843193054,
'token': 57557,
'token_str': 'Rwanda'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa',
'score': 0.07211585342884064,
'token': 57824,
'token_str': 'Burundi'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.029844321310520172,
'token': 10688,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa',
'score': 0.0265930388122797,
'token': 38052,
'token_str': 'Senegal'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | sw_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.80 | 89.36
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
bert-base-multilingual-cased-finetuned-swahili
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-swahili is a Swahili BERT model obtained by fine-tuning bert-base-multilingual-cased model on Swahili language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Swahili CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 86.80, sw\_bert F1: 89.36
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.80, sw\\_bert F1: 89.36",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.80, sw\\_bert F1: 89.36",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
44,
27,
127,
15
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 86.80, sw\\_bert F1: 89.36### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: wo
datasets:
---
# bert-base-multilingual-cased-finetuned-wolof
## Model description
**bert-base-multilingual-cased-finetuned-wolof** is a **Wolof BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Wolof language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Wolof corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-wolof')
>>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal [MASK] ak Afrik.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | wo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 64.52 | 69.43
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: wo
datasets:
---
bert-base-multilingual-cased-finetuned-wolof
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-wolof is a Wolof BERT model obtained by fine-tuning bert-base-multilingual-cased model on Wolof language texts. It provides better performance than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Wolof corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 64.52, wo\_bert F1: 69.43
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 64.52, wo\\_bert F1: 69.43",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 64.52, wo\\_bert F1: 69.43",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
36,
27,
144,
15
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 64.52, wo\\_bert F1: 69.43### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
---
# bert-base-multilingual-cased-finetuned-yoruba
## Model description
**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
[{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746,
'token': 12176,
'token_str': 'Mary'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092,
'token': 13704,
'token_str': 'Queen'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615,
'token': 14382,
'token_str': 'ti'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525,
'token': 11515,
'token_str': 'King'},
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962,
'token': 14005,
'token_str': 'Lady'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | yo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 82.58
[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: yo
datasets:
---
bert-base-multilingual-cased-finetuned-yoruba
=============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-yoruba is a Yoruba BERT model obtained by fine-tuning bert-base-multilingual-cased model on Yorùbá language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, mBERT F1: 78.97, yo\_bert F1: 82.58
Dataset: BBC Yorùbá Textclass, mBERT F1: 75.13, yo\_bert F1: 79.11
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 78.97, yo\\_bert F1: 82.58\nDataset: BBC Yorùbá Textclass, mBERT F1: 75.13, yo\\_bert F1: 79.11",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 78.97, yo\\_bert F1: 82.58\nDataset: BBC Yorùbá Textclass, mBERT F1: 75.13, yo\\_bert F1: 79.11",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
42,
27,
205,
15
] | [
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, mBERT F1: 78.97, yo\\_bert F1: 82.58\nDataset: BBC Yorùbá Textclass, mBERT F1: 75.13, yo\\_bert F1: 79.11### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language:
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# bert-base-multilingual-cased-masakhaner
## Model description
**bert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned mBERT base model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
## Eval results on Test set (F-score)
language|F1-score
-|-
hau |88.66
ibo |85.72
kin |71.94
lug |81.73
luo |77.39
pcm |88.96
swa |88.23
wol |66.27
yor |80.09
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
| {} | token-classification | Davlan/bert-base-multilingual-cased-masakhaner | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
bert-base-multilingual-cased-masakhaner
=======================================
Model description
-----------------
bert-base-multilingual-cased-masakhaner is the first Named Entity Recognition model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned mBERT base model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.
Eval results on Test set (F-score)
----------------------------------
### BibTeX entry and citation info
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------",
"### BibTeX entry and citation info"
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"### BibTeX entry and citation info"
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"passage: TAGS\n#transformers #pytorch #tf #bert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------### BibTeX entry and citation info"
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# bert-base-multilingual-cased-ner-hrl
## Model description
**bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
The training data for the 10 languages are from:
Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code. | {"license": "afl-3.0"} | token-classification | Davlan/bert-base-multilingual-cased-ner-hrl | [
"transformers",
"pytorch",
"tf",
"onnx",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #onnx #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
bert-base-multilingual-cased-ner-hrl
====================================
Model description
-----------------
bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
The training data for the 10 languages are from:
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
"TAGS\n#transformers #pytorch #tf #onnx #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
56,
23,
154
] | [
"passage: TAGS\n#transformers #pytorch #tf #onnx #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# byt5-base-eng-yor-mt
## Model description
**byt5-base-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning byt5-base achieves **12.23 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/byt5-base-eng-yor-mt | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# byt5-base-eng-yor-mt
## Model description
byt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá.
Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning byt5-base achieves 12.23 BLEU on Menyo-20k test set while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
| [
"# byt5-base-eng-yor-mt",
"## Model description\nbyt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 12.23 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# byt5-base-eng-yor-mt",
"## Model description\nbyt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 12.23 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
65,
11,
93,
32,
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"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# byt5-base-eng-yor-mt## Model description\nbyt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 12.23 BLEU on Menyo-20k test set while mt5-base achieves 9.82### BibTeX entry and citation info\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# byt5-base-yor-eng-mt
## Model description
**byt5-base-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning byt5-base achieves 14.05 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/byt5-base-yor-eng-mt | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# byt5-base-yor-eng-mt
## Model description
byt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English.
Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning byt5-base achieves 14.05 BLEU on Menyo-20k test set while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
| [
"# byt5-base-yor-eng-mt",
"## Model description\nbyt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 14.05 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# byt5-base-yor-eng-mt",
"## Model description\nbyt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 14.05 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
56,
11,
93,
32,
22,
13,
41,
15
] | [
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# byt5-base-yor-eng-mt## Model description\nbyt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning byt5-base achieves 14.05 BLEU on Menyo-20k test set while mt5-base achieves 15.57### BibTeX entry and citation info\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language:
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# bert-base-multilingual-cased-masakhaner
## Model description
**distilbert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
## Eval results on Test set (F-score)
language|F1-score
-|-
hau |88.88
ibo |84.87
kin |74.19
lug |78.43
luo |73.32
pcm |87.98
swa |86.20
wol |64.67
yor |78.10
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
| {} | token-classification | Davlan/distilbert-base-multilingual-cased-masakhaner | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
bert-base-multilingual-cased-masakhaner
=======================================
Model description
-----------------
distilbert-base-multilingual-cased-masakhaner is the first Named Entity Recognition model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.
Eval results on Test set (F-score)
----------------------------------
### BibTeX entry and citation info
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------",
"### BibTeX entry and citation info"
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"### BibTeX entry and citation info"
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# distilbert-base-multilingual-cased-ner-hrl
## Model description
**distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
The training data for the 10 languages are from:
Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code. | {"license": "afl-3.0"} | token-classification | Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"transformers",
"pytorch",
"tf",
"safetensors",
"distilbert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #distilbert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
distilbert-base-multilingual-cased-ner-hrl
==========================================
Model description
-----------------
distilbert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
The training data for the 10 languages are from:
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #distilbert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
59,
23,
154
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #distilbert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# m2m100_418M-eng-yor-mt
## Model description
**m2m100_418M-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt).
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning m2m100_418M achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/m2m100_418M-eng-yor-mt | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# m2m100_418M-eng-yor-mt
## Model description
m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá.
Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning m2m100_418M achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
| [
"# m2m100_418M-eng-yor-mt",
"## Model description\nm2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us \n",
"# m2m100_418M-eng-yor-mt",
"## Model description\nm2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
50,
16,
113,
32,
21,
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46,
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] | [
"passage: TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us \n# m2m100_418M-eng-yor-mt## Model description\nm2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82### BibTeX entry and citation info\nBy David Adelani"
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] |
null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# m2m100_418M-eng-yor-mt
## Model description
**m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt).
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/m2m100_418M-yor-eng-mt | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# m2m100_418M-eng-yor-mt
## Model description
m2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English.
Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning m2m100_418M achieves 16.76 BLEU on Menyo-20k test set while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
| [
"# m2m100_418M-eng-yor-mt",
"## Model description\nm2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 16.76 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# m2m100_418M-eng-yor-mt",
"## Model description\nm2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 16.76 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
54,
16,
113,
32,
21,
13,
46,
15
] | [
"passage: TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# m2m100_418M-eng-yor-mt## Model description\nm2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k.#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning m2m100_418M achieves 16.76 BLEU on Menyo-20k test set while mt5-base achieves 15.57### BibTeX entry and citation info\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_yoruba_adr
## Model description
**mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for ADR.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("")
model = AutoModelForTokenClassification.from_pretrained("")
nlp = pipeline("", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564)
70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani
```
```
| {} | text2text-generation | Davlan/mT5_base_yoruba_adr | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2003.10564",
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language: yo
datasets:
- JW300 + Menyo-20k
---
# mT5_base_yoruba_adr
## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for ADR.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
64.63 BLEU on Global Voices test set
70.27 BLEU on Menyo-20k test set
### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani
| [
"# mT5_base_yoruba_adr",
"## Model description\nmT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for ADR.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n64.63 BLEU on Global Voices test set\n70.27 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy Jesujoba Alabi and David Adelani"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_base_yoruba_adr",
"## Model description\nmT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for ADR.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n64.63 BLEU on Global Voices test set\n70.27 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy Jesujoba Alabi and David Adelani"
] | [
66,
12,
108,
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] | [
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mT5_base_yoruba_adr## Model description\nmT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k## Intended uses & limitations#### How to use\nYou can use this model with Transformers *pipeline* for ADR.#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset## Training procedure\nThis model was trained on a single NVIDIA V100 GPU## Eval results on Test set (BLEU score)\n64.63 BLEU on Global Voices test set\n70.27 BLEU on Menyo-20k test set### BibTeX entry and citation info\nBy Jesujoba Alabi and David Adelani"
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mbart50-large-eng-yor-mt
## Model description
**mbart50-large-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning mbarr50-large achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/mbart50-large-eng-yor-mt | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mbart50-large-eng-yor-mt
## Model description
mbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from English to Yorùbá.
Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning mbarr50-large achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82
### BibTeX entry and citation info
By David Adelani
| [
"# mbart50-large-eng-yor-mt",
"## Model description\nmbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning mbarr50-large achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mbart50-large-eng-yor-mt",
"## Model description\nmbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning mbarr50-large achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
51,
14,
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32,
22,
13,
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"passage: TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mbart50-large-eng-yor-mt## Model description\nmbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning mbarr50-large achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82### BibTeX entry and citation info\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mbart50-large-yor-eng-mt
## Model description
**mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves **15.88 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/mbart50-large-yor-eng-mt | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mbart50-large-yor-eng-mt
## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English.
Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
| [
"# mbart50-large-yor-eng-mt",
"## Model description\nmbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart50-large-yor-eng-mt",
"## Model description\nmbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.",
"#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\nFine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
47,
14,
161,
32,
22,
13,
44,
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] | [
"passage: TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us \n# mbart50-large-yor-eng-mt## Model description\nmbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.#### Limitations and bias\nThis model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)\nFine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57### BibTeX entry and citation info\nBy David Adelani"
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] |
null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_eng_yor_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer
model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/mt5_base_eng_yor_mt | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mT5_base_eng_yor_mt
## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and Menyo-20k dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set
### BibTeX entry and citation info
By David Adelani
| [
"# mT5_base_eng_yor_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for MT.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n9.82 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_base_eng_yor_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for MT.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n9.82 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
57,
13,
98,
9,
22,
47,
22,
15,
23,
15
] | [
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mT5_base_eng_yor_mt## Model description\nmT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k## Intended uses & limitations#### How to use\nYou can use this model with Transformers *pipeline* for MT.#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 corpus and Menyo-20k dataset## Training procedure\nThis model was trained on a single NVIDIA V100 GPU## Eval results on Test set (BLEU score)\n9.82 BLEU on Menyo-20k test set### BibTeX entry and citation info\nBy David Adelani"
] | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_yor_eng_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer
model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
15.57 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
### BibTeX entry and citation info
By David Adelani
```
```
| {} | text2text-generation | Davlan/mt5_base_yor_eng_mt | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mT5_base_yor_eng_mt
## Model description
mT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
15.57 BLEU on Menyo-20k test set
### BibTeX entry and citation info
By David Adelani
| [
"# mT5_base_yor_eng_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for MT.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n15.57 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_base_yor_eng_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k",
"## Intended uses & limitations",
"#### How to use\nYou can use this model with Transformers *pipeline* for MT.",
"#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.",
"## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset",
"## Training procedure\nThis model was trained on a single NVIDIA V100 GPU",
"## Eval results on Test set (BLEU score)\n15.57 BLEU on Menyo-20k test set",
"### BibTeX entry and citation info\nBy David Adelani"
] | [
57,
13,
98,
9,
22,
47,
26,
15,
23,
15
] | [
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mT5_base_yor_eng_mt## Model description\nmT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k## Intended uses & limitations#### How to use\nYou can use this model with Transformers *pipeline* for MT.#### Limitations and bias\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.## Training data\nThis model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset## Training procedure\nThis model was trained on a single NVIDIA V100 GPU## Eval results on Test set (BLEU score)\n15.57 BLEU on Menyo-20k test set### BibTeX entry and citation info\nBy David Adelani"
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] |
null | null | transformers | Hugging Face's logo
---
language:
- hau
- ibo
- pcm
- yor
- multilingual
---
# naija-twitter-sentiment-afriberta-large
## Model description
**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta_large large model.
It achieves the **state-of-the-art performance** for the twitter sentiment classification task trained on the [NaijaSenti corpus](https://github.com/hausanlp/NaijaSenti).
The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from [NaijaSenti](https://github.com/hausanlp/NaijaSenti) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers for Sentiment Classification.
```python
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = "I like you"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
id2label = {0:"positive", 1:"neutral", 2:"negative"}
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
#### Limitations and bias
This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.
## Training procedure
This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the [original NaijaSenti paper](https://arxiv.org/abs/2201.08277).
## Eval results on Test set (F-score), average over 5 runs.
language|F1-score
-|-
hau |81.2
ibo |80.8
pcm |74.5
yor |80.4
### BibTeX entry and citation info
```
@inproceedings{Muhammad2022NaijaSentiAN,
title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil},
year={2022}
}
```
| {} | text-classification | Davlan/naija-twitter-sentiment-afriberta-large | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2201.08277"
] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #arxiv-2201.08277 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* hau
* ibo
* pcm
* yor
* multilingual
---
naija-twitter-sentiment-afriberta-large
=======================================
Model description
-----------------
naija-twitter-sentiment-afriberta-large is the first multilingual twitter sentiment classification model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta\_large large model.
It achieves the state-of-the-art performance for the twitter sentiment classification task trained on the NaijaSenti corpus.
The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from NaijaSenti dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers for Sentiment Classification.
#### Limitations and bias
This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.
Training procedure
------------------
This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper.
Eval results on Test set (F-score), average over 5 runs.
--------------------------------------------------------
### BibTeX entry and citation info
| [
"#### How to use\n\n\nYou can use this model with Transformers for Sentiment Classification.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper.\n\n\nEval results on Test set (F-score), average over 5 runs.\n--------------------------------------------------------",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #arxiv-2201.08277 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers for Sentiment Classification.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper.\n\n\nEval results on Test set (F-score), average over 5 runs.\n--------------------------------------------------------",
"### BibTeX entry and citation info"
] | [
56,
20,
93,
11
] | [
"passage: TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #arxiv-2201.08277 #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### How to use\n\n\nYou can use this model with Transformers for Sentiment Classification.#### Limitations and bias\n\n\nThis model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper.\n\n\nEval results on Test set (F-score), average over 5 runs.\n--------------------------------------------------------### BibTeX entry and citation info"
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null | null | transformers | Hugging Face's logo
---
language: am
datasets:
---
# xlm-roberta-base-finetuned-amharic
## Model description
**xlm-roberta-base-finetuned-amharic** is a **Amharic RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Amharic language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Amharic corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa')
>>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን <mask> መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | am_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 70.96 | 77.97
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-amharic | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: am
datasets:
---
xlm-roberta-base-finetuned-amharic
==================================
Model description
-----------------
xlm-roberta-base-finetuned-amharic is a Amharic RoBERTa model obtained by fine-tuning xlm-roberta-base model on Amharic language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Amharic corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Amharic CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 70.96, am\_roberta F1: 77.97
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 70.96, am\\_roberta F1: 77.97",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 70.96, am\\_roberta F1: 77.97",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
127,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Amharic CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 70.96, am\\_roberta F1: 77.97### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
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null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# xlm-roberta-base-finetuned-hausa
## Model description
**xlm-roberta-base-finetuned-hausa** is a **Hausa RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Hausa corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa')
>>> unmasker("Shugaban <mask> Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")
[{'sequence': '<s> Shugaban kasa Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>',
'score': 0.8104371428489685,
'token': 29762,
'token_str': '▁kasa'},
{'sequence': '<s> Shugaban Najeriya Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.17371904850006104,
'token': 49173,
'token_str': '▁Najeriya'},
{'sequence': '<s> Shugaban kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.006917025428265333,
'token': 21221,
'token_str': '▁kasar'},
{'sequence': '<s> Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.005785710643976927,
'token': 72620,
'token_str': '▁Nigeria'},
{'sequence': '<s> Shugaban Kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.0010596115607768297,
'token': 170255,
'token_str': '▁Kasar'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | ha_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.10 | 91.47
[VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | |
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-hausa | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
xlm-roberta-base-finetuned-hausa
================================
Model description
-----------------
xlm-roberta-base-finetuned-hausa is a Hausa RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Hausa corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Hausa CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 86.10, ha\_roberta F1: 91.47
Dataset: VOA Hausa Textclass, XLM-R F1: , ha\_roberta F1:
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 86.10, ha\\_roberta F1: 91.47\nDataset: VOA Hausa Textclass, XLM-R F1: , ha\\_roberta F1:",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 86.10, ha\\_roberta F1: 91.47\nDataset: VOA Hausa Textclass, XLM-R F1: , ha\\_roberta F1:",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
150,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Hausa CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 86.10, ha\\_roberta F1: 91.47\nDataset: VOA Hausa Textclass, XLM-R F1: , ha\\_roberta F1:### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: ig
datasets:
---
# xlm-roberta-base-finetuned-igbo
## Model description
**xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-igbo')
>>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | ig_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 87.74
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-igbo | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ig
datasets:
---
xlm-roberta-base-finetuned-igbo
===============================
Model description
-----------------
xlm-roberta-base-finetuned-igbo is a Igbo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 84.51, ig\_roberta F1: 87.74
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 84.51, ig\\_roberta F1: 87.74",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 84.51, ig\\_roberta F1: 87.74",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
147,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 84.51, ig\\_roberta F1: 87.74### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: rw
datasets:
---
# xlm-roberta-base-finetuned-kinyarwanda
## Model description
**xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-kinyarwanda')
>>> unmasker("Twabonye ko igihe mu <mask> hazaba hari ikirango abantu bakunze")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | rw_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 73.22 | 77.76
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: rw
datasets:
---
xlm-roberta-base-finetuned-kinyarwanda
======================================
Model description
-----------------
xlm-roberta-base-finetuned-kinyarwanda is a Kinyarwanda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Kinyarwanda language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 73.22, rw\_roberta F1: 77.76
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 73.22, rw\\_roberta F1: 77.76",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 73.22, rw\\_roberta F1: 77.76",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
136,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 73.22, rw\\_roberta F1: 77.76### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: lg
datasets:
---
# xlm-roberta-base-finetuned-luganda
## Model description
**xlm-roberta-base-finetuned-luganda** is a **Luganda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luganda language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luganda')
>>> unmasker("Ffe tulwanyisa abo abaagala okutabangula <mask>, Kimuli bwe yategeezezza.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | lg_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 79.69 | 84.70
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-luganda | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: lg
datasets:
---
xlm-roberta-base-finetuned-luganda
==================================
Model description
-----------------
xlm-roberta-base-finetuned-luganda is a Luganda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luganda language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 79.69, lg\_roberta F1: 84.70
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 79.69, lg\\_roberta F1: 84.70",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 79.69, lg\\_roberta F1: 84.70",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
137,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 79.69, lg\\_roberta F1: 84.70### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: luo
datasets:
---
# xlm-roberta-base-finetuned-luo
## Model description
**xlm-roberta-base-finetuned-luo** is a **Luo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luo language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luo corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luo')
>>> unmasker("Obila ma Changamwe <mask> pedho achije angwen mag njore")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | luo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.86 | 75.27
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-luo | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: luo
datasets:
---
xlm-roberta-base-finetuned-luo
==============================
Model description
-----------------
xlm-roberta-base-finetuned-luo is a Luo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luo language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luo corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 74.86, luo\_roberta F1: 75.27
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 74.86, luo\\_roberta F1: 75.27",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 74.86, luo\\_roberta F1: 75.27",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
126,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 74.86, luo\\_roberta F1: 75.27### BibTeX entry and citation info\n\n\nBy David Adelani"
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] |
null | null | transformers | Hugging Face's logo
---
language: pcm
datasets:
---
# xlm-roberta-base-finetuned-naija
## Model description
**xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-naija')
>>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | pcm_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-naija | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: pcm
datasets:
---
xlm-roberta-base-finetuned-naija
================================
Model description
-----------------
xlm-roberta-base-finetuned-naija is a Nigerian Pidgin RoBERTa model obtained by fine-tuning xlm-roberta-base model on Nigerian Pidgin language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on JW300 + BBC Pidgin
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.26, pcm\\_roberta F1: 90.00",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.26, pcm\\_roberta F1: 90.00",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
131,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on JW300 + BBC Pidgin\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.26, pcm\\_roberta F1: 90.00### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
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null | null | transformers | Hugging Face's logo
---
language: sw
datasets:
---
# xlm-roberta-base-finetuned-swahili
## Model description
**xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa")
[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa',
'score': 0.5077782273292542,
'token': 190096,
'token_str': 'Ufaransa'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.3657738268375397,
'token': 7270,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa',
'score': 0.01592041552066803,
'token': 176392,
'token_str': 'Gabon'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.010881908237934113,
'token': 9942,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa',
'score': 0.009554869495332241,
'token': 185918,
'token_str': 'Marseille'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | sw_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.55 | 89.46
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-swahili | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: sw
datasets:
---
xlm-roberta-base-finetuned-swahili
==================================
Model description
-----------------
xlm-roberta-base-finetuned-swahili is a Swahili RoBERTa model obtained by fine-tuning xlm-roberta-base model on Swahili language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Swahili CC-100
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 87.55, sw\_roberta F1: 89.46
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.55, sw\\_roberta F1: 89.46",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.55, sw\\_roberta F1: 89.46",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
45,
27,
130,
15
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Swahili CC-100\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 87.55, sw\\_roberta F1: 89.46### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
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null | null | transformers | Hugging Face's logo
---
language: wo
datasets:
---
# xlm-roberta-base-finetuned-wolof
## Model description
**xlm-roberta-base-finetuned-luganda** is a **Wolof RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Wolof language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Wolof corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-wolof')
>>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal <mask> ak Afrik.")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | wo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 63.86 | 68.31
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-wolof | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: wo
datasets:
---
xlm-roberta-base-finetuned-wolof
================================
Model description
-----------------
xlm-roberta-base-finetuned-luganda is a Wolof RoBERTa model obtained by fine-tuning xlm-roberta-base model on Wolof language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Wolof corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 63.86, wo\_roberta F1: 68.31
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 63.86, wo\\_roberta F1: 68.31",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 63.86, wo\\_roberta F1: 68.31",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
147,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online)\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 63.86, wo\\_roberta F1: 68.31### BibTeX entry and citation info\n\n\nBy David Adelani"
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null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
---
# xlm-roberta-base-finetuned-yoruba
## Model description
**xlm-roberta-base-finetuned-yoruba** is a **Yoruba RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Yorùbá language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-yoruba')
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ <mask> Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
[{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.24844281375408173,
'token': 44109,
'token_str': '▁Queen'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ile Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.1665010154247284,
'token': 1350,
'token_str': '▁ile'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.07604238390922546,
'token': 1053,
'token_str': '▁ti'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ baba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.06353845447301865,
'token': 12878,
'token_str': '▁baba'},
{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Oba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.03836742788553238,
'token': 82879,
'token_str': '▁Oba'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | yo_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 77.58 | 83.66
[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | |
### BibTeX entry and citation info
By David Adelani
```
```
| {} | fill-mask | Davlan/xlm-roberta-base-finetuned-yoruba | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: yo
datasets:
---
xlm-roberta-base-finetuned-yoruba
=================================
Model description
-----------------
xlm-roberta-base-finetuned-yoruba is a Yoruba RoBERTa model obtained by fine-tuning xlm-roberta-base model on Yorùbá language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá corpus.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------
Dataset: MasakhaNER, XLM-R F1: 77.58, yo\_roberta F1: 83.66
Dataset: BBC Yorùbá Textclass, XLM-R F1: , yo\_roberta F1:
### BibTeX entry and citation info
By David Adelani
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 77.58, yo\\_roberta F1: 83.66\nDataset: BBC Yorùbá Textclass, XLM-R F1: , yo\\_roberta F1:",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 77.58, yo\\_roberta F1: 83.66\nDataset: BBC Yorùbá Textclass, XLM-R F1: , yo\\_roberta F1:",
"### BibTeX entry and citation info\n\n\nBy David Adelani"
] | [
40,
27,
206,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU\n\n\nEval results on Test set (F-score, average over 5 runs)\n-------------------------------------------------------\n\n\nDataset: MasakhaNER, XLM-R F1: 77.58, yo\\_roberta F1: 83.66\nDataset: BBC Yorùbá Textclass, XLM-R F1: , yo\\_roberta F1:### BibTeX entry and citation info\n\n\nBy David Adelani"
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] |
null | null | transformers | Hugging Face's logo
---
language:
- am
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-base-masakhaner
## Model description
**xlm-roberta-base-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
| {} | token-classification | Davlan/xlm-roberta-base-masakhaner | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* am
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
xlm-roberta-base-masakhaner
===========================
Model description
-----------------
xlm-roberta-base-masakhaner is the first Named Entity Recognition model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.
### BibTeX entry and citation info
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.",
"### BibTeX entry and citation info"
] | [
49,
23,
218,
11
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.### BibTeX entry and citation info"
] | [
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-base-ner-hrl
## Model description
**xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
The training data for the 10 languages are from:
Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code. | {"license": "afl-3.0"} | token-classification | Davlan/xlm-roberta-base-ner-hrl | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
xlm-roberta-base-ner-hrl
========================
Model description
-----------------
xlm-roberta-base-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
The training data for the 10 languages are from:
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
54,
23,
154
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
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] |
null | null | transformers | Hugging Face's logo
---
language:
- af
- nr
- nso
- ss
- st
- tn
- ts
- ve
- xh
- zu
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-base-sadilar-ner
## Model description
**xlm-roberta-base-sadilar-ner** is the first **Named Entity Recognition** model for 10 South African languages (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of South African languages datasets obtained from [SADILAR](https://www.sadilar.org/index.php/en/) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Kuchaza kona ukuthi uMengameli uMnuz Cyril Ramaphosa, usebatshelile ukuthi uzosikhipha maduze isitifiketi."
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) [SADILAR](https://www.sadilar.org/index.php/en/) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
### BibTeX entry and citation info
```
| {} | token-classification | Davlan/xlm-roberta-base-sadilar-ner | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* af
* nr
* nso
* ss
* st
* tn
* ts
* ve
* xh
* zu
* multilingual
datasets:
* masakhaner
---
xlm-roberta-base-sadilar-ner
============================
Model description
-----------------
xlm-roberta-base-sadilar-ner is the first Named Entity Recognition model for 10 South African languages (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of South African languages datasets obtained from SADILAR dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) SADILAR dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
### BibTeX entry and citation info
'''
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) SADILAR dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:",
"### BibTeX entry and citation info\n\n\n'''"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) SADILAR dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:",
"### BibTeX entry and citation info\n\n\n'''"
] | [
41,
23,
175,
13
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) SADILAR dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:### BibTeX entry and citation info\n\n\n'''"
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- as
- bn
- ca
- en
- es
- eu
- fr
- gu
- hi
- id
- ig
- mr
- pa
- pt
- sw
- ur
- vi
- yo
- zh
- multilingual
datasets:
- wikiann
---
# xlm-roberta-base-wikiann-ner
## Model description
**xlm-roberta-base-wikiann-ner** is the first **Named Entity Recognition** model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of languages datasets obtained from [WikiANN](https://huggingface.co/datasets/wikiann) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base-wikiann-ner")
model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)[wikiann](https://huggingface.co/datasets/wikiann).
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
### BibTeX entry and citation info
```
| {} | token-classification | Davlan/xlm-roberta-base-wikiann-ner | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* as
* bn
* ca
* en
* es
* eu
* fr
* gu
* hi
* id
* ig
* mr
* pa
* pt
* sw
* ur
* vi
* yo
* zh
* multilingual
datasets:
* wikiann
---
xlm-roberta-base-wikiann-ner
============================
Model description
-----------------
xlm-roberta-base-wikiann-ner is the first Named Entity Recognition model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of languages datasets obtained from WikiANN dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann.
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
### BibTeX entry and citation info
'''
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann.\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:",
"### BibTeX entry and citation info\n\n\n'''"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann.\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:",
"### BibTeX entry and citation info\n\n\n'''"
] | [
49,
23,
184,
13
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann.\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:### BibTeX entry and citation info\n\n\n'''"
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null | null | transformers | Hugging Face's logo
---
language:
- amh
- hau
- ibo
- kin
- lug
- luo
- pcm
- swa
- wol
- yor
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-large-masakhaner
## Model description
**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
## Eval results on Test set (F-score)
language|F1-score
-|-
amh |75.76
hau |91.75
ibo |86.26
kin |76.38
lug |84.64
luo |80.65
pcm |89.55
swa |89.48
wol |70.70
yor |82.05
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
| {} | token-classification | Davlan/xlm-roberta-large-masakhaner | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* amh
* hau
* ibo
* kin
* lug
* luo
* pcm
* swa
* wol
* yor
* multilingual
datasets:
* masakhaner
---
xlm-roberta-large-masakhaner
============================
Model description
-----------------
xlm-roberta-large-masakhaner is the first Named Entity Recognition model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset.
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.
Eval results on Test set (F-score)
----------------------------------
### BibTeX entry and citation info
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------",
"### BibTeX entry and citation info"
] | [
57,
23,
233,
11
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus.\n\n\nEval results on Test set (F-score)\n----------------------------------### BibTeX entry and citation info"
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-large-ner-hrl
## Model description
**xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
The training data for the 10 languages are from:
Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code. | {"license": "afl-3.0"} | token-classification | Davlan/xlm-roberta-large-ner-hrl | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
xlm-roberta-large-ner-hrl
=========================
Model description
-----------------
xlm-roberta-large-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages
Intended uses & limitations
---------------------------
#### How to use
You can use this model with Transformers *pipeline* for NER.
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
-------------
The training data for the 10 languages are from:
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Training procedure
------------------
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
| [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
] | [
57,
23,
154
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data\n-------------\n\n\nThe training data for the 10 languages are from:\n\n\n\nThe training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:\n\n\n\nTraining procedure\n------------------\n\n\nThis model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code."
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] |
null | null | transformers |
# Iron Man DialoGPT Model | {"tags": ["conversational"]} | text-generation | Dawit/DialogGPT-small-ironman | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Iron Man DialoGPT Model | [
"# Iron Man DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Iron Man DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Iron Man DialoGPT Model"
] | [
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null | null | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | text-generation | Daymarebait/Discord_BOT_RICK | [
"transformers",
"conversational",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #conversational #endpoints_compatible #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #conversational #endpoints_compatible #region-us \n",
"# My Awesome Model"
] | [
21,
4
] | [
"passage: TAGS\n#transformers #conversational #endpoints_compatible #region-us \n# My Awesome Model"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emoBERTTamil
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9666
- Accuracy: 0.671
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1128 | 1.0 | 250 | 1.0290 | 0.672 |
| 1.0226 | 2.0 | 500 | 1.0172 | 0.686 |
| 0.9137 | 3.0 | 750 | 0.9666 | 0.671 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tamilmixsentiment"], "metrics": ["accuracy"], "model_index": [{"name": "emoBERTTamil", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "tamilmixsentiment", "type": "tamilmixsentiment", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.671}}]}]} | text-classification | DeadBeast/emoBERTTamil | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:tamilmixsentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| emoBERTTamil
============
This model is a fine-tuned version of bert-base-uncased on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9666
* Accuracy: 0.671
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] | [
63,
98,
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34
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
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] |
null | null | transformers |
# **Korean-mBERT**
This model is a fine-tune checkpoint of mBERT-base-cased over **Hugging Face Kore_Scm** dataset for Text classification.
### **How to use?**
**Task**: binary-classification
- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)
Click on **Use in Transformers**!
| {"language": "korean", "license": "apache-2.0", "datasets": ["Korean-Sarcasm"]} | text-classification | DeadBeast/korscm-mBERT | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:Korean-Sarcasm",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"korean"
] | TAGS
#transformers #pytorch #bert #text-classification #dataset-Korean-Sarcasm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Korean-mBERT
This model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.
### How to use?
Task: binary-classification
- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)
Click on Use in Transformers!
| [
"# Korean-mBERT\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.",
"### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)\n- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)\n\nClick on Use in Transformers!"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #dataset-Korean-Sarcasm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Korean-mBERT\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.",
"### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)\n- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)\n\nClick on Use in Transformers!"
] | [
55,
38,
69
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #dataset-Korean-Sarcasm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Korean-mBERT\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)\n- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)\n\nClick on Use in Transformers!"
] | [
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null | null | transformers |
# **mBERT-base-cased-finetuned-bengali-fakenews**
This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.
### **How to use?**
**Task**: binary-classification
- LABEL_1: Authentic (*Authentic means news is authentic*)
- LABEL_0: Fake (*Fake means news is fake*)
```
from transformers import pipeline
print(pipeline("sentiment-analysis",model="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews",tokenizer="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews")("অভিনেতা আফজাল শরীফকে ২০ লাখ টাকার অনুদান অসুস্থ অভিনেতা আফজাল শরীফকে চিকিৎসার জন্য ২০ লাখ টাকা অনুদান দিয়েছেন প্রধানমন্ত্রী শেখ হাসিনা।"))
``` | {"language": "bengali", "license": "apache-2.0", "datasets": ["BanFakeNews"]} | text-classification | DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:BanFakeNews",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bengali"
] | TAGS
#transformers #pytorch #bert #text-classification #dataset-BanFakeNews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBERT-base-cased-finetuned-bengali-fakenews
This model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.
### How to use?
Task: binary-classification
- LABEL_1: Authentic (*Authentic means news is authentic*)
- LABEL_0: Fake (*Fake means news is fake*)
| [
"# mBERT-base-cased-finetuned-bengali-fakenews\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.",
"### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Authentic (*Authentic means news is authentic*)\n- LABEL_0: Fake (*Fake means news is fake*)"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #dataset-BanFakeNews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBERT-base-cased-finetuned-bengali-fakenews\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.",
"### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Authentic (*Authentic means news is authentic*)\n- LABEL_0: Fake (*Fake means news is fake*)"
] | [
52,
76,
47
] | [
"passage: TAGS\n#transformers #pytorch #bert #text-classification #dataset-BanFakeNews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# mBERT-base-cased-finetuned-bengali-fakenews\n\nThis model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Authentic (*Authentic means news is authentic*)\n- LABEL_0: Fake (*Fake means news is fake*)"
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] |
null | null | null | ---
Summarisation model summarsiation | {} | null | Dean/summarsiation | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| ---
Summarisation model summarsiation | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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null | null | transformers |
#Scaramouche DialoGPT Model | {"tags": ["conversational"]} | text-generation | DecafNosebleed/DialoGPT-small-ScaraBot | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Scaramouche DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# Model Card for ChemBERTa-10M-MTR
# Model Details
## Model Description
More information needed
- **Developed by:** DeepChem
- **Shared by [Optional]:** DeepChem
- **Model type:** Token Classification
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** [RoBERTa](https://huggingface.co/roberta-base?text=The+goal+of+life+is+%3Cmask%3E.)
- **Resources for more information:** More information needed
# Uses
## Direct Use
More information needed.
## Downstream Use [Optional]
More information needed.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
More information needed
## Training Procedure
### Preprocessing
More information needed
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed
### Factors
More information needed
### Metrics
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed.
# Citation
**BibTeX:**
```bibtex
@book{Ramsundar-et-al-2019,
title={Deep Learning for the Life Sciences},
author={Bharath Ramsundar and Peter Eastman and Patrick Walters and Vijay Pande and Karl Leswing and Zhenqin Wu},
publisher={O'Reilly Media},
note={\url{https://www.amazon.com/Deep-Learning-Life-Sciences-Microscopy/dp/1492039837}},
year={2019}
}
```
**APA:**
More information needed
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
DeepChem in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, RobertaForRegression
tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-10M-MTR")
model = RobertaForRegression.from_pretrained("DeepChem/ChemBERTa-10M-MTR")
```
</details>
| {"tags": ["roberta"]} | null | DeepChem/ChemBERTa-10M-MTR | [
"transformers",
"pytorch",
"roberta",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for ChemBERTa-10M-MTR
# Model Details
## Model Description
More information needed
- Developed by: DeepChem
- Shared by [Optional]: DeepChem
- Model type: Token Classification
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: RoBERTa
- Resources for more information: More information needed
# Uses
## Direct Use
More information needed.
## Downstream Use [Optional]
More information needed.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
More information needed
## Training Procedure
### Preprocessing
More information needed
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed
### Factors
More information needed
### Metrics
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed.
BibTeX:
APA:
More information needed
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
DeepChem in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
</details>
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"# Environmental Impact\n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed",
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"# Glossary [optional]\n \nMore information needed",
"# More Information [optional]\nMore information needed",
"# Model Card Authors [optional]\n \nDeepChem in collaboration with Ezi Ozoani and the Hugging Face team",
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null | null | transformers | RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer | {} | feature-extraction | DeepChem/SmilesTokenizer_PubChem_1M | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer | [] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n"
] | [
30
] | [
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | # GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.
## Corpus
This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).
## Tokenizer
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.
This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.
Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training.
## Training
The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.
## Authors
The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).
Thanks to the members of the community who collaborated with funding for the initial tests.
## Cautions
The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.
| {"language": "es", "license": "mit", "tags": ["GPT-2", "Spanish", "ebooks", "nlg"], "datasets": ["ebooks"], "widget": [{"text": "Quisiera saber que va a suceder"}]} | text-generation | DeepESP/gpt2-spanish-medium | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"es",
"dataset:ebooks",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.
## Corpus
This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).
## Tokenizer
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.
This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.
Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training.
## Training
The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.
## Authors
The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).
Thanks to the members of the community who collaborated with funding for the initial tests.
## Cautions
The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.
| [
"# GPT2-Spanish\nGPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.",
"## Corpus\nThis model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).",
"## Tokenizer\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.\n\nThis tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.\n\nApart from the special token \"<|endoftext|>\" for text ending in the OpenAI GPT-2 models, the tokens \"<|talk|>\", \"<|ax1|>\", \"<|ax2|>\" (..)\"<|ax9|>\" were included so that they can serve as prompts in future training.",
"## Training\nThe model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.",
"## Authors\nThe model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).\n\nThanks to the members of the community who collaborated with funding for the initial tests.",
"## Cautions\nThe model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content."
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-Spanish\nGPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.",
"## Corpus\nThis model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).",
"## Tokenizer\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.\n\nThis tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.\n\nApart from the special token \"<|endoftext|>\" for text ending in the OpenAI GPT-2 models, the tokens \"<|talk|>\", \"<|ax1|>\", \"<|ax2|>\" (..)\"<|ax9|>\" were included so that they can serve as prompts in future training.",
"## Training\nThe model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.",
"## Authors\nThe model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).\n\nThanks to the members of the community who collaborated with funding for the initial tests.",
"## Cautions\nThe model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content."
] | [
79,
74,
51,
194,
36,
92,
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] | [
"passage: TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT2-Spanish\nGPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.## Corpus\nThis model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization).## Tokenizer\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.\n\nThis tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.\n\nApart from the special token \"<|endoftext|>\" for text ending in the OpenAI GPT-2 models, the tokens \"<|talk|>\", \"<|ax1|>\", \"<|ax2|>\" (..)\"<|ax9|>\" were included so that they can serve as prompts in future training.## Training\nThe model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers."
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