davanstrien/model_cards_with_readmes · Datasets at Hugging Face

alkiskoudounas/sd-butterflies-64px

alkiskoudounas

null

6

2

diffusers

0

unconditional-image-generation

true

false

mit

null

0

['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class']

false

true

529

false

# Model Card for Stable Diffusion - Butterflies, 64px Model developed for the Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class). This model is a diffusion model for unconditional image generation of cute butterflies 🦋. It is trained on a very small collection of 1'000 pictures and trained for 30 epochs. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('alkiskoudounas/sd-butterflies-64px') image = pipeline().images[0] image ```

c0312552c82b562e5dd697ecc7ded993

google/multiberts-seed_4-step_1900k

google

bert

8

14

transformers

0

null

true

false

apache-2.0

['en']

null

0

['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1900k']

false

true

3,527

false

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1900k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #4, captured at step 1900k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1900k') model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_1900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1900k') model = BertModel.from_pretrained("google/multiberts-seed_4-step_1900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

810bb0c75c89a845ba973490474a252e

sentence-transformers/facebook-dpr-question_encoder-single-nq-base

sentence-transformers

bert

13

906

sentence-transformers

0

sentence-similarity

true

false

apache-2.0

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

false

true

2,745

false

# sentence-transformers/facebook-dpr-question_encoder-single-nq-base This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [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/facebook-dpr-question_encoder-single-nq-base') 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 def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # 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/facebook-dpr-question_encoder-single-nq-base') model = AutoModel.from_pretrained('sentence-transformers/facebook-dpr-question_encoder-single-nq-base') # 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 = cls_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/facebook-dpr-question_encoder-single-nq-base) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Have a look at: [DPR Model](https://github.com/facebookresearch/DPR)

dc15a7ef4120d408b6763e6e87cf232c

techiaith/wav2vec2-xlsr-ft-cy

techiaith

wav2vec2

13

2,968

transformers

3

automatic-speech-recognition

true

false

apache-2.0

['cy']

['common_voice']

null

0

['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'ken-lm', 'robust-speech-event', 'speech']

true

1,871

false

# wav2vec2-xlsr-ft-cy Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Welsh Common Voice version 11 dataset](https://commonvoice.mozilla.org/cy/datasets). Source code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at [https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy](https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy). ## Usage The wav2vec2-xlsr-ft-cy (acoustic) model can be used directly (without a language model) as follows: ```python import torch import torchaudio import librosa from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy") model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy") audio, rate = librosa.load(audio_file, sr=16000) inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits # greedy decoding predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) ``` ## Using the Language Model See https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/releases/tag/22.10 for more details and examples of a KenLM usage with the Parlance PyTorch CTC decode bindings library: [https://github.com/parlance/ctcdecode](https://github.com/parlance/ctcdecode) ## Evaluation According to the Welsh Common Voice version 11 test set, the WER of techiaith/wav2vec2-xlsr-ft-cy standalone is **6.04%** When assisted by the KenLM language model the same test produces a WER of **4.05%** See: https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/blob/main/train/python/evaluate.py

dc8e390468afe068a5b2577551aa1969

Maltehb/danish-bert-botxo

Maltehb

bert

13

5,694

transformers

3

fill-mask

true

cc-by-4.0

['da']

['common_crawl', 'wikipedia', 'dindebat.dk', 'hestenettet.dk', 'danishOpenSubtitles']

null

0

['danish', 'bert', 'masked-lm', 'Certainly']

false

true

1,055

false

# Danish BERT (version 2, uncased) by [Certainly](https://certainly.io/) (previously known as BotXO). All credit goes to [Certainly](https://certainly.io/) (previously known as BotXO), who developed Danish BERT. For data and training details see their [GitHub repository](https://github.com/certainlyio/nordic_bert) or [this article](https://www.certainly.io/blog/danish-bert-model/). You can also visit their [organization page](https://huggingface.co/Certainly) on Hugging Face. It is both available in TensorFlow and Pytorch format. The original TensorFlow version can be downloaded using [this link](https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1). Here is an example on how to load Danish BERT in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library: ```python from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Maltehb/danish-bert-botxo") model = AutoModelForPreTraining.from_pretrained("Maltehb/danish-bert-botxo") ```

2ea93cff1bc963c29707ec2333c6f9ab

sberbank-ai/mGPT

sberbank-ai

gpt2

7

8,519

transformers

98

text-generation

true

false

apache-2.0

['en', 'az', 'sw', 'af', 'ar', 'ba', 'be', 'bxr', 'bg', 'bn', 'cv', 'hy', 'da', 'de', 'el', 'es', 'eu', 'fa', 'fi', 'fr', 'he', 'hi', 'hu', 'kk', 'id', 'it', 'ja', 'ka', 'ky', 'ko', 'lt', 'lv', 'mn', 'ml', 'os', 'mr', 'ms', 'my', 'nl', 'ro', 'pl', 'pt', 'sah', 'ru', 'tg', 'sv', 'ta', 'te', 'tk', 'th', 'tr', 'tl', 'tt', 'tyv', 'uk', 'en', 'ur', 'vi', 'uz', 'yo', 'zh', 'xal']

['mc4', 'wikipedia']

null

1

0

['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron']

false

true

3,172

false

# Multilingual GPT model We introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 60 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, [Deepspeed](https://github.com/microsoft/DeepSpeed) and [Megatron](https://github.com/NVIDIA/Megatron-LM) frameworks allows us to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released [XGLM](https://arxiv.org/pdf/2112.10668.pdf) models at the same time covering more languages and enhancing NLP possibilities for low resource languages. ## Code The source code for the mGPT XL model is available on [Github](https://github.com/sberbank-ai/mgpt) ## Paper mGPT: Few-Shot Learners Go Multilingual [Abstract](https://arxiv.org/abs/2204.07580) [PDF](https://arxiv.org/pdf/2204.07580.pdf) ![](https://habrastorage.org/webt/1q/ru/yt/1qruytul6m2m-upyk9frq3pgrds.png) ``` @misc{https://doi.org/10.48550/arxiv.2204.07580, doi = {10.48550/ARXIV.2204.07580}, url = {https://arxiv.org/abs/2204.07580}, author = {Shliazhko, Oleh and Fenogenova, Alena and Tikhonova, Maria and Mikhailov, Vladislav and Kozlova, Anastasia and Shavrina, Tatiana}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2; I.2.7, 68-06, 68-04, 68T50, 68T01}, title = {mGPT: Few-Shot Learners Go Multilingual}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ## Languages Model supports 60 languages: ISO codes: ```az, sw, af, ar, ba, be, bxr, bg, bn, cv, hy, da, de, el, es, eu, fa, fi, fr, he, hi, hu, kk, id, it, ja, ka, ky, ko, lt, lv, mn, ml, os, mr, ms, my, nl, ro, pl, pt, sah, ru, tg, sv, ta, te, tk, th, tr, tl, tt, tyv, uk, en, ur, vi, uz, yo, zh, xal``` Languages: ```Afrikaans, Azerbaijani, Belarusian, Bengali, Chuvash, German, English, Basque, Finnish, Hebrew (modern), Hungarian, Indonesian, Japanese, Kazakh, Kirghiz, Kyrgyz, Latvian, Mongolian, Malay, Dutch, Polish, Romanian, Moldavan, Yakut, Swahili, Telugu, Thai, Turkish, Tuvinian, Urdu, Vietnamese, Yoruba, Arabic, Bashkir, Bulgarian, Buriat, Danish, Greek, Modern, Spanish; Castilian, Persian, French, Hindi, Armenian, Italian, Georgian, Korean, Lithuanian, Malayalam, Marathi, Burmese, Ossetian, Ossetic, Portuguese, Russian, Swedish, Tamil, Tajik, Turkmen, Tatar, Ukrainian, Uzbek, Kalmyk, Chinese``` ## Training Data Statistics - Size: 488 Billion UTF characters <img style="text-align:center; display:block;" src="https://huggingface.co/sberbank-ai/mGPT/resolve/main/stats.png"> "General training corpus statistics" ## Details The model was trained with sequence length 512 using Megatron and Deepspeed libs by [SberDevices](https://sberdevices.ru/) team on a dataset of 600 GB of texts in 60 languages. The model has seen 440 billion BPE tokens in total. Total training time was around 12 days on 256 Nvidia V100 GPUs.

3ab3caf4740255c734a61b02c3468f84

Helsinki-NLP/opus-mt-af-fr

Helsinki-NLP

marian

10

55

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-af-fr * source languages: af * target languages: fr * OPUS readme: [af-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.af.fr | 35.3 | 0.543 |

9b17b204cd64abb2b02d01493163455d

kadirnar/bsrgan

kadirnar

null

3

0

null

0

null

false

apache-2.0

null

1

0

['Super-Resolution', 'computer-vision', 'bsrgan', 'gan']

false

true

933

false

### Model Description [BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .](https://arxiv.org/abs/2103.14006) [BSRGAN-Pip: Packaged version of the BSRGAN repository](https://github.com/kadirnar/bsrgan-pip/) [Paper Repo: Implementation of paper - BSRGAN](https://github.com/cszn/BSRGAN) ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/bsrgan', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ```

48f6eb8dc9acbea1d527d205fbf82085

s87204/distilbert-base-uncased-finetuned-cola

s87204

distilbert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

1

0

['generated_from_trainer']

true

1,572

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8505 - Matthews Correlation: 0.5365 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5201 | 1.0 | 535 | 0.5345 | 0.4153 | | 0.3469 | 2.0 | 1070 | 0.5033 | 0.5109 | | 0.2367 | 3.0 | 1605 | 0.6589 | 0.5209 | | 0.1705 | 4.0 | 2140 | 0.7778 | 0.5354 | | 0.125 | 5.0 | 2675 | 0.8505 | 0.5365 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

f77dbb161c6a1dc76985145f2215c060

microsoft/tapex-base

microsoft

bart

8

809

transformers

10

table-question-answering

true

false

mit

['en']

null

0

['tapex', 'table-question-answering']

false

true

2,701

false

# TAPEX (base-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). ## Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. ## Intended Uses You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you. ### How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base") model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) # tapex accepts uncased input since it is pre-trained on the uncased corpus query = "select year where city = beijing" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # ['2008'] ``` ### How to Fine-tuning Please find the fine-tuning script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex). ### BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ```

b93f1d56b445abae8be6da0b55d702df

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_128

gokuls

mobilebert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,861

false

# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4833 - Accuracy: 0.8544 ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5233 | 1.0 | 8748 | 0.5730 | 0.8291 | | 0.3614 | 2.0 | 17496 | 0.5357 | 0.8394 | | 0.3019 | 3.0 | 26244 | 0.5166 | 0.8509 | | 0.268 | 4.0 | 34992 | 0.5172 | 0.8509 | | 0.2465 | 5.0 | 43740 | 0.4833 | 0.8544 | | 0.2313 | 6.0 | 52488 | 0.5422 | 0.8463 | | 0.2201 | 7.0 | 61236 | 0.5778 | 0.8303 | | 0.2113 | 8.0 | 69984 | 0.5364 | 0.8417 | | 0.204 | 9.0 | 78732 | 0.5428 | 0.8314 | | 0.198 | 10.0 | 87480 | 0.5442 | 0.8337 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

99b8057fa20dec1e4e65370dd058c0fa

dxiao/bert-finetuned-ner-100percent

dxiao

bert

12

18

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,526

false

# bert-finetuned-ner-100percent This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5711 - Precision: 0.8227 - Recall: 0.8498 - F1: 0.8360 - Accuracy: 0.9254 ## 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: 8 - eval_batch_size: 8 - seed: 2022 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 75 | 0.5329 | 0.8228 | 0.8438 | 0.8332 | 0.9277 | | No log | 2.0 | 150 | 0.5674 | 0.8110 | 0.8438 | 0.8271 | 0.9242 | | No log | 3.0 | 225 | 0.5711 | 0.8227 | 0.8498 | 0.8360 | 0.9254 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

9ef5227855bdc1c02f7adbba1e0205af

SiriRRR/test-model

SiriRRR

bart

4

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

828

false

# test-model This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

cf57aeae7a8deb82c94f8a9feda3d2e3

anas-awadalla/t5-small-few-shot-k-32-finetuned-squad-seed-4

anas-awadalla

t5

15

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

963

false

# t5-small-few-shot-k-32-finetuned-squad-seed-4 This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset. ## 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: 4 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

2fd2e72adc4ab9d2032e8327a6e66727

hisaoka/bart-large-cnn_radiology-ai-cardiothoracic-0.9

hisaoka

bart

11

5

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,052

false

# bart-large-cnn_radiology-ai-cardiothoracic-0.9 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. ## 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: 3 - eval_batch_size: 3 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1

a276a3795f8c06049c98e46778d1ff8f

Yidhar/dream

Yidhar

null

9

0

null

0

null

false

openrail

['en']

null

0

[]

false

true

893

false

![xy_grid-0047-2936604364-1girl.jpg](https://s3.amazonaws.com/moonup/production/uploads/1675246540285-639ae7f4b49b7262559746f4.jpeg) ![xy_grid-0045-365243663-outdoor,street_scene,18th_century,modern_Shanghai,((Colonial style)),roadside_cafe,blue_umbrella,chairs_and_tables,glass_window,.jpg](https://s3.amazonaws.com/moonup/production/uploads/1675246627374-639ae7f4b49b7262559746f4.jpeg) ![00379-419404114-hat, witch_hat, solo, simple_background, white_background, au_ra, horns, upper_body, closed_mouth, witch, dragon_horns,アウラ,Aura,.png](https://s3.amazonaws.com/moonup/production/uploads/1675246664355-639ae7f4b49b7262559746f4.png) ![xyz_grid-0021-3399619172-masterpiece,best quality,asterpiece,beautiful,extremely detailed CG unity 8k wallpaper,foucs on face,girl,solo,messy hair,grey h.jpg](https://s3.amazonaws.com/moonup/production/uploads/1675504050224-639ae7f4b49b7262559746f4.jpeg)

caef81d2034bd95472df2e8ab7c30482

db0/microworlds

db0

null

4

0

null

6

text-to-image

false

creativeml-openrail-m

null

0

1

0

['stable-diffusion', 'text-to-image']

false

true

1,088

false

This is a fork of the Microworlds model provided in [Public Prompts](https://publicprompts.art/microworlds-dreambooth-model/) for easier download and integration into services as allowed by the CreativeML-Openrails-M license ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

95c3cce9b857bc33a5a6173a6c4838b3

johntang/finetuning-sentiment-model-3000-samples

johntang

distilbert

10

9

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

1

0

['generated_from_trainer']

true

1,049

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3426 - Accuracy: 0.8767 - F1: 0.8787 ## 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: 2 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

bb6fe34d1f38eafa42b7de98741623bd

vendorabc/modeltest

vendorabc

null

5

0

sklearn

0

tabular-classification

false

mit

null

0

['sklearn', 'skops', 'tabular-classification']

false

true

19,191

false

# Model description This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |---------------------------------|----------------------------------------------------------| | aggressive_elimination | False | | cv | 5 | | error_score | nan | | estimator__categorical_features | | | estimator__early_stopping | auto | | estimator__l2_regularization | 0.0 | | estimator__learning_rate | 0.1 | | estimator__loss | auto | | estimator__max_bins | 255 | | estimator__max_depth | | | estimator__max_iter | 100 | | estimator__max_leaf_nodes | 31 | | estimator__min_samples_leaf | 20 | | estimator__monotonic_cst | | | estimator__n_iter_no_change | 10 | | estimator__random_state | | | estimator__scoring | loss | | estimator__tol | 1e-07 | | estimator__validation_fraction | 0.1 | | estimator__verbose | 0 | | estimator__warm_start | False | | estimator | HistGradientBoostingClassifier() | | factor | 3 | | max_resources | auto | | min_resources | exhaust | | n_jobs | -1 | | param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} | | random_state | 42 | | refit | True | | resource | n_samples | | return_train_score | True | | scoring | | | verbose | 0 | </details> ### Model Plot The model plot is below. <style>#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 {color: black;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 pre{padding: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable {background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-item {z-index: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:only-child::after {width: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-text-repr-fallback {display: none;}</style><div id="sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ab167486-be7e-4eb5-be01-ba21adbd7469" type="checkbox" ><label for="ab167486-be7e-4eb5-be01-ba21adbd7469" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e9df9f06-8d9e-4379-ad72-52f461408663" type="checkbox" ><label for="e9df9f06-8d9e-4379-ad72-52f461408663" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.959064 | | f1 score | 0.959064 | # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import pickle with open(pkl_filename, 'rb') as file: clf = pickle.load(file) ``` </details> # Model Card Authors This model card is written by following authors: skops_user # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` bibtex @inproceedings{...,year={2020}} ``` # Additional Content ## Confusion matrix ![Confusion matrix](confusion_matrix.png) ## Hyperparameter search results <details> <summary> Click to expand </summary> | iter | n_resources | mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_max_depth | param_max_leaf_nodes | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | split0_train_score | split1_train_score | split2_train_score | split3_train_score | split4_train_score | mean_train_score | std_train_score | |--------|---------------|-----------------|----------------|-------------------|------------------|-------------------|------------------------|-----------------------------------------|---------------------|---------------------|---------------------|---------------------|---------------------|-------------------|------------------|-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|--------------------|-------------------| | 0 | 44 | 0.0498069 | 0.0107112 | 0.0121156 | 0.0061838 | 2 | 5 | {'max_depth': 2, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0492636 | 0.0187271 | 0.00738611 | 0.00245441 | 2 | 10 | {'max_depth': 2, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0572055 | 0.0153176 | 0.0111395 | 0.0010297 | 2 | 15 | {'max_depth': 2, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0498482 | 0.0177091 | 0.00857358 | 0.00415935 | 5 | 5 | {'max_depth': 5, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0500658 | 0.00992094 | 0.00998321 | 0.00527031 | 5 | 10 | {'max_depth': 5, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0525903 | 0.0151616 | 0.00874681 | 0.00462998 | 5 | 15 | {'max_depth': 5, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0512018 | 0.0130152 | 0.00881834 | 0.00500514 | 10 | 5 | {'max_depth': 10, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.0566921 | 0.0186051 | 0.00513492 | 0.000498488 | 10 | 10 | {'max_depth': 10, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 0 | 44 | 0.060587 | 0.04041 | 0.00987453 | 0.00529624 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | | 1 | 132 | 0.232459 | 0.0479878 | 0.0145514 | 0.00856422 | 10 | 5 | {'max_depth': 10, 'max_leaf_nodes': 5} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | | 1 | 132 | 0.272297 | 0.0228833 | 0.011561 | 0.0068272 | 10 | 10 | {'max_depth': 10, 'max_leaf_nodes': 10} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | | 1 | 132 | 0.239161 | 0.0330412 | 0.0116591 | 0.003554 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | | 2 | 396 | 0.920334 | 0.18198 | 0.0166654 | 0.00776263 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.962025 | 0.911392 | 0.987342 | 0.974359 | 0.935897 | 0.954203 | 0.0273257 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | </details> ## Classification report <details> <summary> Click to expand </summary> | index | precision | recall | f1-score | support | |--------------|-------------|----------|------------|-----------| | malignant | 0.951613 | 0.936508 | 0.944 | 63 | | benign | 0.963303 | 0.972222 | 0.967742 | 108 | | macro avg | 0.957458 | 0.954365 | 0.955871 | 171 | | weighted avg | 0.958996 | 0.959064 | 0.958995 | 171 | </details>

79c2152cc812a97d8963da8b6bcbb71c

MaCoCu/XLMR-BERTovski

MaCoCu

xlm-roberta

10

21

transformers

0

feature-extraction

true

cc0-1.0

['bg', 'mk', 'multilingual']

null

1

0

1

0

['BERTovski', 'MaCoCu']

false

true

5,510

false

# Model description **XLMR-BERTovski** is a large pre-trained language model trained on Bulgarian and Macedonian texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik van Noord](https://www.rikvannoord.nl/) from the University of Groningen. XLMR-BERTovski was trained on 74GB of Bulgarian and Macedonian text, which is equal to just over 7 billion tokens. It was trained for 67,500 steps with a batch size of 1,024, which was approximately 2.5 epochs. It uses the same vocabulary as the original XLMR-large model. The model is trained on the same data as [BERTovski](https://huggingface.co/RVN/BERTovski), but this model was trained from scratch using the RoBERTa architecture. The training and fine-tuning procedures are described in detail on our [Github repo](https://github.com/macocu/LanguageModels). # How to use ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("RVN/XLMR-BERTovski") model = AutoModel.from_pretrained("RVN/XLMR-BERTovski") # PyTorch model = TFAutoModel.from_pretrained("RVN/XLMR-BERTovski") # Tensorflow ``` # Data For training, we used all Bulgarian and Macedonian data that was present in the [MaCoCu](https://macocu.eu/), Oscar, mc4 and Wikipedia corpora. In a manual analysis we found that for Oscar and mc4, if the data did not come from the corresponding domain (.bg or .mk), it was often (badly) machine translated. Therefore, we opted to only use data that originally came from a .bg or .mk domain. After de-duplicating the data, we were left with a total of 54.5 GB of Bulgarian and 9 GB of Macedonian text. Since there was quite a bit more Bulgarian data, we simply doubled the Macedonian data during training. # Benchmark performance We tested performance of XLMR-BERTovski on benchmarks of XPOS, UPOS and NER. For Bulgarian, we used the data from the [Universal Dependencies](https://universaldependencies.org/) project. For Macedonian, we used the data sets created in the [babushka-bench](https://github.com/clarinsi/babushka-bench/) project. We also tested on a Google (Bulgarian) and human (Macedonian) translated version of the COPA data set (for details see our [Github repo](https://github.com/RikVN/COPA)). We compare performance to [BERTovski](https://huggingface.co/RVN/BERTovski) and the strong multi-lingual models XLMR-base and XLMR-large. For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels). Scores are averages of three runs, except for COPA, for which we use 10 runs. We use the same hyperparameter settings for all models for UPOS/XPOS/NER, for COPA we optimized the learning rate on the dev set. ## Bulgarian | | **UPOS** | **UPOS** | **XPOS** | **XPOS** | **NER** | **NER** | **COPA** | |-----------------|:--------:|:--------:|:--------:|:--------:|:-------:|:--------:|:--------:| | | **Dev** | **Test** | **Dev** | **Test** | **Dev** | **Test** | **Test** | | **XLM-R-base** | 99.2 | 99.4 | 98.0 | 98.3 | 93.2 | 92.9 | 56.9 | | **XLM-R-large** | 99.3 | 99.4 | 97.4 | 97.7 | 93.7 | 93.5 | 53.1 | | **BERTovski** | 98.8 | 99.1 | 97.6 | 97.8 | 93.5 | 93.3 | 51.7 | | **XLMR-BERTovski** | 99.3 | 99.5 | 98.5 | 98.8 | 94.4 | 94.3 | 54.6 | ## Macedonian | | **UPOS** | **UPOS** | **XPOS** | **XPOS** | **NER** | **NER** | **COPA** | |-----------------|:--------:|:--------:|:--------:|:--------:|:-------:|:--------:|:--------:| | | **Dev** | **Test** | **Dev** | **Test** | **Dev** | **Test** | **Test** | | **XLM-R-base** | 98.3 | 98.6 | 97.3 | 97.1 | 92.8 | 94.8 | 55.3 | | **XLM-R-large** | 98.3 | 98.7 | 97.7 | 97.5 | 93.3 | 95.1 | 52.5 | | **BERTovski** | 97.8 | 98.1 | 96.4 | 96.0 | 92.8 | 94.6 | 51.8 | | **XLMR-BERTovski** | 98.6 | 98.8 | 98.0 | 97.7 | 94.4 | 96.3 | 55.6| # Acknowledgements Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). The authors received funding from the European Union's Connecting Europe Facility 2014- 2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu). # Citation If you use this model, please cite the following paper: ```bibtex @inproceedings{non-etal-2022-macocu, title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages", author = "Ba{\~n}{\'o}n, Marta and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Garc{\'\i}a-Romero, Cristian and Kuzman, Taja and Ljube{\v{s}}i{\'c}, Nikola and van Noord, Rik and Sempere, Leopoldo Pla and Ram{\'\i}rez-S{\'a}nchez, Gema and Rupnik, Peter and Suchomel, V{\'\i}t and Toral, Antonio and van der Werff, Tobias and Zaragoza, Jaume", booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation", month = jun, year = "2022", address = "Ghent, Belgium", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2022.eamt-1.41", pages = "303--304" } ```

b0c981cd52746401d75b2331c5d45c96

anas-awadalla/roberta-base-few-shot-k-64-finetuned-squad-seed-0

anas-awadalla

roberta

17

5

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

985

false

# roberta-base-few-shot-k-64-finetuned-squad-seed-0 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. ## 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: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

1df1ba0eff9b1f234e22b5a7089cfdb3

coldfir3/xlm-roberta-base-finetuned-panx-en

coldfir3

xlm-roberta

9

12

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3925 - F1: 0.7075 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1493 | 1.0 | 50 | 0.5884 | 0.4748 | | 0.5135 | 2.0 | 100 | 0.4088 | 0.6623 | | 0.3558 | 3.0 | 150 | 0.3925 | 0.7075 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

0bdd233591d24353cfa2274b4aa0ac6e

Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa

Intel

bert

8

10

transformers

0

fill-mask

true

false

apache-2.0

['en']

['wikipedia', 'bookcorpus']

null

0

['fill-mask']

false

true

456

false

# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA This model is was created using Prune OFA method described in [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021. For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).

2d7a5c2037c582eed264d9c5f9c6804c

scasutt/Prototype_training_large_model

scasutt

wav2vec2

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,380

false

# Prototype_training_large_model This model is a fine-tuned version of [scasutt/Prototype_training_large_model](https://huggingface.co/scasutt/Prototype_training_large_model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2585 - Wer: 1.0 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.0545 | 1.47 | 100 | 3.2604 | 1.0 | | 3.0413 | 2.93 | 200 | 3.2585 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

012d84d8dcf15feb701ef3d364103d48

muhtasham/bert-small-finetuned-cuad-longer

muhtasham

bert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['cuad']

null

0

['generated_from_trainer']

true

1,661

false

# bert-small-finetuned-cuad-longer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.0617 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0601 | 1.0 | 8702 | 0.0404 | | 0.0551 | 2.0 | 17404 | 0.0394 | | 0.0428 | 3.0 | 26106 | 0.0481 | | 0.0375 | 4.0 | 34808 | 0.0425 | | 0.0403 | 5.0 | 43510 | 0.0551 | | 0.0246 | 6.0 | 52212 | 0.0588 | | 0.0284 | 7.0 | 60914 | 0.0557 | | 0.0303 | 8.0 | 69616 | 0.0543 | | 0.0239 | 9.0 | 78318 | 0.0634 | | 0.0207 | 10.0 | 87020 | 0.0617 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

923f09efefab314107b467ad24ec3f55

kadirnar/yolox_tiny-v0.1.1

kadirnar

null

3

0

null

0

object-detection

false

apache-2.0

null

['detection-datasets/coco']

null

0

['object-detection', 'computer-vision', 'yolox', 'yolov3', 'yolov5']

false

true

1,203

false

### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_tiny-v0.1.1", config_path = "configs.yolox_tiny", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```

ba885c8242a9d0107ddb4ff5d74b8545

Toshifumi/distilbert-base-multilingual-cased-finetuned-emotion

Toshifumi

distilbert

12

4

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,378

false

# distilbert-base-multilingual-cased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3702 - Accuracy: 0.8885 - F1: 0.8888 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1646 | 1.0 | 250 | 0.6190 | 0.8085 | 0.7992 | | 0.4536 | 2.0 | 500 | 0.3702 | 0.8885 | 0.8888 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

0a5674aae2c3defb84e429e550b95a25

anuragshas/wav2vec2-large-xls-r-300m-pa-in

anuragshas

wav2vec2

19

9

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['pa']

['mozilla-foundation/common_voice_7_0']

null

0

['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard']

true

2,939

false

# XLS-R-300M - Punjabi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2548 - Wer: 0.5677 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.12 - num_epochs: 120 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 6.4804 | 16.65 | 400 | 1.8461 | 1.0 | | 0.474 | 33.33 | 800 | 1.1018 | 0.6624 | | 0.1389 | 49.98 | 1200 | 1.1918 | 0.6103 | | 0.0919 | 66.65 | 1600 | 1.1889 | 0.6058 | | 0.0657 | 83.33 | 2000 | 1.2266 | 0.5931 | | 0.0479 | 99.98 | 2400 | 1.2512 | 0.5902 | | 0.0355 | 116.65 | 2800 | 1.2548 | 0.5677 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-pa-in --dataset mozilla-foundation/common_voice_7_0 --config pa-IN --split test ``` ### Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "anuragshas/wav2vec2-large-xls-r-300m-pa-in" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "pa-IN", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text # => "ਉਨ੍ਹਾਂ ਨੇ ਸਾਰੇ ਤੇਅਰਵੇ ਵੱਖਰੀ ਕਿਸਮ ਦੇ ਕੀਤੇ ਹਨ" ``` ### Eval results on Common Voice 7 "test" (WER): | Without LM | With LM (run `./eval.py`) | |---|---| | 51.968 | 45.611 |

16362b1b287711ffb5ae45959853a9a7

gopalkalpande/t5-small-finetuned-bbc-news-summarization

gopalkalpande

t5

18

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,548

false

# gopalkalpande/t5-small-finetuned-bbc-news-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7637 - Validation Loss: 0.3528 - Train Rouge1: 19.4783 - Train Rouge2: 13.2994 - Train Rougel: 17.4791 - Train Rougelsum: 17.6204 - Train Gen Len: 19.0 - Epoch: 0 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 4e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.7637 | 0.3528 | 19.4783 | 13.2994 | 17.4791 | 17.6204 | 19.0 | 0 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

c15be7ca14597c641bc03cf21c567ba9

ahsanjavid/convnext-tiny-finetuned-cifar10

ahsanjavid

convnext

5

28

transformers

1

image-classification

true

false

apache-2.0

null

['cifar10']

null

0

1

0

['vision', 'image-classification']

false

true

525

false

# ConvNext-tiny-finetuned-cifar10 (tiny-sized model) ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). Convnext tiny finetuned on cifar 10 dataset. Which has ten classes. Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.

fa329d7ce402ae006ef9c0cfc638e896

transformerCommunity/rojtburd

transformerCommunity

null

11

0

null

0

null

false

mit

null

0

[]

false

true

1,222

false

### rojtburd on Stable Diffusion This is the `<Rojtburd>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<Rojtburd> 0](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/0.jpeg) ![<Rojtburd> 1](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/3.jpeg) ![<Rojtburd> 2](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/4.jpeg) ![<Rojtburd> 3](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/5.jpeg) ![<Rojtburd> 4](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/1.jpeg) ![<Rojtburd> 5](https://huggingface.co/transformerCommunity/rojtburd/resolve/main/concept_images/2.jpeg)

3a70dfcd031ee4b96c7fb11242483204

Ivor22/distilbert-base-uncased-finetuned-sst2

Ivor22

distilbert

25

115

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,742

false

# distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5776 - Precision: 0.9038 - Recall: 0.9099 - Accuracy: 0.9048 - F1: 0.9068 ## 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 | Precision | Recall | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:| | 0.0237 | 1.0 | 4210 | 0.6639 | 0.8685 | 0.9369 | 0.8956 | 0.9014 | | 0.0247 | 2.0 | 8420 | 0.5776 | 0.9038 | 0.9099 | 0.9048 | 0.9068 | | 0.0304 | 3.0 | 12630 | 0.6533 | 0.8839 | 0.9257 | 0.9002 | 0.9043 | | 0.0281 | 4.0 | 16840 | 0.6654 | 0.8877 | 0.9257 | 0.9025 | 0.9063 | | 0.0095 | 5.0 | 21050 | 0.7832 | 0.8710 | 0.9279 | 0.8933 | 0.8986 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

ebb9c76af2957697a98a9e3312b4a296

Jersonm89/Avatar

Jersonm89

null

23

473

diffusers

15

text-to-image

false

creativeml-openrail-m

null

2

0

2

0

['stable-diffusion', 'text-to-image']

false

true

824

false

Model trained with images from James Cameron's Avatar movie. Draw avatar characters with facial features of the person indicated at the prompt ### Sample images Will Smith: prompt= portrait Will Smith male, avatar style ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/will_1.png) ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/will_2.png) ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/will_3.png) ### Sample images Johnny Depp: prompt= portrait Johnny Depp male, avatar style ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/jonny_1.png) ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/jonny_2.png) ![output Samples v3](https://huggingface.co/Jersonm89/Avatar/resolve/main/jonny_3.png)

02e7d14882355576248b48337423066b

emmyapi/distilbart-podimo-data-eval-2-2e

emmyapi

bart

13

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,590

false

# distilbart-podimo-data-eval-2-2e This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7374 - Rouge1: 32.9705 - Rouge2: 6.9494 - Rougel: 17.922 - Rougelsum: 29.4629 - Gen Len: 137.5363 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 4.1649 | 0.98 | 44 | 3.8138 | 32.12 | 6.544 | 17.5999 | 28.8314 | 136.4553 | | 3.6772 | 1.98 | 88 | 3.7374 | 32.9705 | 6.9494 | 17.922 | 29.4629 | 137.5363 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1

b269a0d5f34f0e744dc108246c310abe

cnut1648/biolinkbert-mednli

cnut1648

bert

13

131

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,570

false

# biolinkbert-mednli This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on [MedNLI](https://physionet.org/content/mednli/1.0.0/). It achieves the following results on the evaluation set: ``` { "eval_accuracy": 0.8788530230522156, "eval_loss": 0.7843484878540039, "eval_runtime": 39.7009, "eval_samples": 1395, "eval_samples_per_second": 35.138, "eval_steps_per_second": 1.108 } ``` The accuracy for the test set is ``` { "eval_accuracy": 0.8607594966888428, "eval_loss": 0.879707932472229, "eval_runtime": 27.4404, "eval_samples": 1395, "eval_samples_per_second": 51.821, "eval_steps_per_second": 1.64 } ``` The labels are ``` "id2label": { "0": "entailment", "1": "neutral", "2": "contradiction" }, ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.22.2 - Pytorch 1.13.0+cu117 - Datasets 2.4.0 - Tokenizers 0.12.1

acc477875c495cc2265e50d60a888e78

paola-md/recipe-reg

paola-md

roberta

8

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,832

false

# recipe-reg This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3717 - Rmse: 1.8362 - Mse: 3.3717 - Mae: 1.6145 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:| | 3.3247 | 1.0 | 12809 | 3.3717 | 1.8362 | 3.3717 | 1.6145 | | 3.3238 | 2.0 | 25618 | 3.3722 | 1.8363 | 3.3722 | 1.6145 | | 3.3217 | 3.0 | 38427 | 3.3718 | 1.8362 | 3.3718 | 1.6145 | | 3.3215 | 4.0 | 51236 | 3.3754 | 1.8372 | 3.3754 | 1.6145 | | 3.3203 | 5.0 | 64045 | 3.3721 | 1.8363 | 3.3721 | 1.6145 | | 3.3199 | 6.0 | 76854 | 3.3731 | 1.8366 | 3.3731 | 1.6145 | | 3.319 | 7.0 | 89663 | 3.3731 | 1.8366 | 3.3731 | 1.6145 | | 3.3188 | 8.0 | 102472 | 3.3717 | 1.8362 | 3.3717 | 1.6145 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

be0c84904487aeab43a5f40c418b60c6

anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-0

anas-awadalla

t5

17

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

965

false

# t5-base-few-shot-k-256-finetuned-squad-infilling-seed-0 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset. ## 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: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

a0f9437c97e00da0d73e5195f9e30ffe

google/mobilenet_v1_0.75_192

google

mobilenet_v1

5

45,426

transformers

0

image-classification

true

false

other

null

['imagenet-1k']

null

0

['vision', 'image-classification']

false

true

2,363

false

# MobileNet V1 MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md). Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v1) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v1_0.75_192") model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v1_0.75_192") inputs = preprocessor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch.

447ab49cd16a8f176cc4b70c47e9e669

sayakpaul/glpn-nyu-finetuned-diode-221116-054332

sayakpaul

glpn

9

1

transformers

0

depth-estimation

true

false

apache-2.0

null

0

['vision', 'depth-estimation', 'generated_from_trainer']

true

1,786

false

# glpn-nyu-finetuned-diode-221116-054332 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.6028 - Rmse: nan ## 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: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | |:-------------:|:-----:|:----:|:---------------:|:----:| | 1.6801 | 1.0 | 72 | 1.3466 | nan | | 1.2071 | 2.0 | 144 | 0.8388 | nan | | 0.7548 | 3.0 | 216 | 0.6771 | nan | | 0.6401 | 4.0 | 288 | 0.6443 | nan | | 0.629 | 5.0 | 360 | 0.6452 | nan | | 0.5892 | 6.0 | 432 | 0.6202 | nan | | 0.5773 | 7.0 | 504 | 0.6262 | nan | | 0.5532 | 8.0 | 576 | 0.6151 | nan | | 0.5268 | 9.0 | 648 | 0.5995 | nan | | 0.5416 | 10.0 | 720 | 0.6028 | nan | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Tokenizers 0.13.2

19354e87764c494a5c434e42235cbb49

MadhuG/vit-base-patch16-224-in21k-lung_cancer

MadhuG

vit

7

3

transformers

0

image-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,709

false

# MadhuG/vit-base-patch16-224-in21k-lung_cancer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1061 - Train Accuracy: 0.1041 - Validation Loss: 1.1028 - Validation Accuracy: 0.1394 - Epoch: 0 ## 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: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 1.1061 | 0.1041 | 1.1028 | 0.1394 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.0 - Datasets 2.7.0 - Tokenizers 0.13.2

c381da0bbc01b8671bf5416d85502412

Hemlok/DateMix

Hemlok

null

10

0

null

36

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'text-to-image']

false

true

2,925

false

# 『Date Mix & RDt Mix』 ![a](Image/DateMix.png) ![a](Image/1.png) - "Date Mix" is a model created by hierarchical merging based on "anything-v4.5"([andite/anything-v4.0 · Hugging Face](https://huggingface.co/andite/anything-v4.0)). ---- ![a](Image/RDtMix.png) ![a](Image/2.png) - "RDtMix" is a merged realistic model based on "Date Mix". ---- # ◆Discord [Join Discord Server](https://discord.gg/eN6aSWRddT) - The merged model community of Hemlok. ---- # ◆About - This model was created to improve composition and color tone. - If you like realistic illustrations, please use "RDtModel". - Sampler: DDIM or DPM++ SDE Karras - Steps: 50~ - Clipskip: 2 - CFG Scale: 5-12 - Denoise strength: 0.5-0.7(As you like) - Negative prompts should be as few as possible. - vae: As you wish. (Any etc. If not used, color may become lighter) ---- # ◆How to use - Please download the file by yourself and use it with WebUI(AUTOMATIC1111) etc. - Use the f16 version for Colab(T4) or a PC with low RAM. ---- # ◆Colab Note [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Bsm7p_Db5u3IiHr3YjTeKu4jOllfTyqZ?usp=sharing) - (I have not checked the operation but it probably works.) ---- # ◆Comparison <img src="https://i.imgur.com/fW2cPsY.jpg" width="1700" height=""> <img src="https://i.imgur.com/0k48okp.jpg" width="1700" height=""> ``` kawaii, 1girl, looking at viewer, smile ``` ---- <img src="https://i.imgur.com/d44iIvs.jpg" width="1700" height=""> ``` (morning), (school), 1girl, solo, looking at viewer, cowboy shot, (school uniform), smile, black hair, stockings ``` ---- # ◆Sampler & CFG Scale <img src="https://i.imgur.com/vzUBzmw.jpg" width="1700" height=""> ``` (morning), (school), 1girl, solo, looking at viewer, cowboy shot, (school uniform), smile, black hair, stockings ``` ---- # Disclaimer - The creation of SFW and NSFW images is at the discretion of the individual creator. - This model is not a model created to publish NSFW content in public places, etc. ---- ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) (Full text of the license: https://huggingface.co/spaces/CompVis/stable-diffusion-license)

3de325aadbf228d77c047bef7acbc78d

Helsinki-NLP/opus-mt-es-pon

Helsinki-NLP

marian

10

11

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-es-pon * source languages: es * target languages: pon * OPUS readme: [es-pon](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-pon/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-pon/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-pon/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-pon/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.pon | 21.6 | 0.448 |

84c3590b4c75ebb3adc4502078528c33

Elytum/bert-finetuned-ner

Elytum

bert

10

8

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,541

false

# bert-finetuned-ner This model is a fine-tuned version of [gaunernst/bert-small-uncased](https://huggingface.co/gaunernst/bert-small-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0186 - Precision: 0.9941 - Recall: 0.9952 - F1: 0.9946 - Accuracy: 0.9963 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0277 | 1.0 | 2500 | 0.0190 | 0.9929 | 0.9939 | 0.9934 | 0.9956 | | 0.0137 | 2.0 | 5000 | 0.0180 | 0.9935 | 0.9951 | 0.9943 | 0.9960 | | 0.0095 | 3.0 | 7500 | 0.0186 | 0.9941 | 0.9952 | 0.9946 | 0.9963 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

d79839588f534d6e4dfc0d1780141bb8

Allenbv/kevin-xd

Allenbv

null

26

7

diffusers

0

text-to-image

false

creativeml-openrail-m

null

2

0

['text-to-image', 'stable-diffusion']

false

true

2,177

false

### Kevin-xd Dreambooth model trained by Allenbv with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: Kevekek ![0](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/descarga_(3).png) ![1](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00093-2988603660-close_portrait_of_futuristic_(kevekek),_kintsugi,_modern_fine_art,_fractal,_intricate,_elegant,_highly_detailed,_digital_photogr.png) ![2](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/descarga_(5).png) ![3](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00030-1415197780-close_portrait_of_(kevekek),_digital_painting,_artstation,_concept_art,_donato_giancola,_Joseph_Christian_Leyendecker,_WLOP,_Bor.png) ![4](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00066-2795391050-A_potrait_of_(kevekek)_as_a_mermaid_,_realistic_shaded,_fine_details._Night_setting._Very_anime_style._Realistic_shaded_lighting.png) ![5](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00067-2795391051-A_potrait_of_(kevekek)_as_a_mermaid_,_realistic_shaded,_fine_details._Night_setting._Very_anime_style._Realistic_shaded_lighting.png) ![6](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00090-3209711875-an_ultradetailed_beautiful_painting_of_(kevekek)_with_colorful_band_aids,_rave_concert_poster,_retro,_conrad_roset,_greg_rutkows.png) ![7](https://huggingface.co/Allenbv/kevin-xd/resolve/main/sample_images/00077-2029398807-beatiful_(kevekek)_wearing_cat_ears_with_big_sack,_anime_key_visual,_intricate,_stunning,_highly_detailed,_digital_painting,_art.png)

9b63d6de907cbee57d0c035444adfacb

BlackKakapo/t5-small-grammar-ro-v2

BlackKakapo

t5

8

34

transformers

1

text2text-generation

true

false

['apache-2.0']

['ro']

null

0

[]

false

true

1,264

false

# Romanian grammar ![v2.0](https://img.shields.io/badge/V.2-06.08.2022-brightgreen) Fine-tune [t5-small-grammar-ro](https://huggingface.co/datasets/BlackKakapo/grammar-ro-v2) model for grammar. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset v2](https://huggingface.co/datasets/BlackKakapo/grammar-ro-v2). The dataset contains ~1600k examples. ### How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/t5-small-grammar-ro-v2") model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/t5-small-grammar-ro-v2") ``` ### Or ```cmd pip install happytransformer ``` ```python from happytransformer import TTSettings, HappyTextToText happy_tt_save = HappyTextToText('T5',r"BlackKakapo/t5-small-grammar-ro-v2") beam_settings = TTSettings(num_beams=10, min_length=10, max_length=100) ``` ### Generate ```python sent = "Interesant cym lucreaza modelu dat, carr a fost antrenat cu un dataset foarre mare." prefix = "grammar: " example = prefix + sent result = happy_tt_save.generate_text(example, args=beam_settings) print(result.text) ``` ### Output ```out Interesant cum lucrează modelul dat, care a fost antrenat cu un dataset foarte mare. ```

ab95b692502041e854c6429f6b20fa2c

fpianz/roberta-english-book-reviews-sentiment

fpianz

roberta

4

7

transformers

0

text-classification

true

false

mit

null

0

[]

false

true

713

false

Three classes sentiment analysis (positive, negative, neutral) Based on https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes Fine-tuned using: - annotated sentences from book reviews in English https://www.gti.uvigo.es/index.php/en/book-reviews-annotated-dataset-for-aspect-based-sentiment-analysis - annotated paragraphs from amateur writers' stories https://arxiv.org/abs/1910.11769 Performance for books: ![hartmann_ft_test_books.png](https://s3.amazonaws.com/moonup/production/uploads/1661430297130-611a6e0f289467cafea62d0e.png) Performance for reviews: ![hartmann_ft_test_reviews.png](https://s3.amazonaws.com/moonup/production/uploads/1661430338990-611a6e0f289467cafea62d0e.png)

e4d326bca185376a18c05d50f64e5b2f

nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2

nlpconnect

bert

9

148

transformers

0

feature-extraction

true

false

apache-2.0

null

0

['generated_from_keras_callback', 'dpr']

true

4,355

false

# dpr-question_encoder_bert_uncased_L-2_H-128_A-2 This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training data: data.retriever.nq-adv-hn-train(facebookresearch/DPR). It achieves the following results on the evaluation set: ## Evaluation data evaluation dataset: facebook-dpr-dev-dataset from official DPR github |model_name|data_name|num of queries|num of passages|R@10|R@20|R@50|R@100|R@100| |---|---|---|---|---|---|---|---|---| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our)|nq-dev dataset|6445|199795|60.53%|68.28%|76.07%|80.98%|91.45%| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our)|nq-dev dataset|6445|199795|65.43%|71.99%|79.03%|83.24%|92.11%| |*facebook/dpr-ctx_encoder-single-nq-base(hf/fb)|nq-dev dataset|6445|199795|40.94%|49.27%|59.05%|66.00%|82.00%| evaluation dataset: UKPLab/beir test data but we have used first 2lac passage only. |model_name|data_name|num of queries|num of passages|R@10|R@20|R@50|R@100|R@100| |---|---|---|---|---|---|---|---|---| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our)|nq-test dataset|3452|200001|49.68%|59.06%|69.40%|75.75%|89.28%| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our)|nq-test dataset|3452|200001|51.62%|61.09%|70.10%|76.07%|88.70%| |*facebook/dpr-ctx_encoder-single-nq-base(hf/fb)|nq-test dataset|3452|200001|32.93%|43.74%|56.95%|66.30%|83.92%| Note: * means we have evaluated on same eval dataset. ### Usage (HuggingFace Transformers) ```python passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2") query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2") p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2") q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2") def get_title_text_combined(passage_dicts): res = [] for p in passage_dicts: res.append(tuple((p['title'], p['text']))) return res processed_passages = get_title_text_combined(passage_dicts) def extracted_passage_embeddings(processed_passages, model_config): passage_inputs = tokenizer.batch_encode_plus( processed_passages, add_special_tokens=True, truncation=True, padding="max_length", max_length=model_config.passage_max_seq_len, return_token_type_ids=True ) passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), np.array(passage_inputs['attention_mask']), np.array(passage_inputs['token_type_ids'])], batch_size=512, verbose=1) return passage_embeddings passage_embeddings = extracted_passage_embeddings(processed_passages, model_config) def extracted_query_embeddings(queries, model_config): query_inputs = tokenizer.batch_encode_plus( queries, add_special_tokens=True, truncation=True, padding="max_length", max_length=model_config.query_max_seq_len, return_token_type_ids=True ) query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), np.array(query_inputs['attention_mask']), np.array(query_inputs['token_type_ids'])], batch_size=512, verbose=1) return query_embeddings query_embeddings = extracted_query_embeddings(queries, model_config) ``` ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Tokenizers 0.10.3

3e1aa0bbc0da61433fd24bddccf3a58f

Danessely/distilroberta-base-finetuned-dna

Danessely

roberta

6

4

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,266

false

# distilroberta-base-finetuned-dna This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1473 ## 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: 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.1615 | 1.0 | 8014 | 1.1578 | | 1.1559 | 2.0 | 16028 | 1.1561 | | 1.1503 | 3.0 | 24042 | 1.1475 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

e18a10f53a13288c4f07178a46cab521

Vandita/Bert-finetuned-Sarcoji_1

Vandita

bert

22

13

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,328

false

# Bert-finetuned-Sarcoji_1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3051 - Accuracy: 0.8933 ## 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: 32 - eval_batch_size: 32 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3476 | 1.0 | 735 | 0.2745 | 0.8764 | | 0.2515 | 2.0 | 1470 | 0.2667 | 0.8896 | | 0.1341 | 3.0 | 2205 | 0.3051 | 0.8933 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

e33f10a21c9a72feef455484f439f58c

pere/whisper-NST-TPU-test2

pere

whisper

28

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,292

false

# whisper-NST-TPU-test2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9943 - Wer: 100.0 ## 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: 8 - seed: 42 - distributed_type: tpu - num_devices: 4 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-----:| | 5.535 | 1.0 | 2 | 4.9943 | 100.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

85a5076ce3c50eb97d5b9000489ea6ac

lmqg/bart-large-squad-qag

lmqg

bart

14

38

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qag_squad']

null

0

['questions and answers generation']

true

3,838

false

# Model Card of `lmqg/bart-large-squad-qag` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/bart-large-squad-qag") # model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-large-squad-qag") output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.16 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedF1Score (MoverScore) | 63.79 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (BERTScore) | 93.21 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (MoverScore) | 66.71 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (BERTScore) | 91.17 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (MoverScore) | 61.32 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 256 - epoch: 14 - batch: 8 - lr: 1e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-squad-qag/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

513d72364a66f5273131469d83e477e3

gokuls/distilbert_sa_GLUE_Experiment_data_aug_rte_192

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,625

false

# distilbert_sa_GLUE_Experiment_data_aug_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.8449 - Accuracy: 0.5199 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2938 | 1.0 | 568 | 1.8449 | 0.5199 | | 0.0556 | 2.0 | 1136 | 2.3009 | 0.4946 | | 0.0295 | 3.0 | 1704 | 2.6341 | 0.5018 | | 0.0183 | 4.0 | 2272 | 3.0449 | 0.5235 | | 0.0119 | 5.0 | 2840 | 3.1349 | 0.5162 | | 0.0091 | 6.0 | 3408 | 3.4876 | 0.5343 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

ffdb84304b172de04c14b522a88d63af

Atallahw/mycatPishi

Atallahw

null

17

4

diffusers

0

text-to-image

true

false

creativeml-openrail-m

null

1

0

['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal']

false

true

747

false

# DreamBooth model for the pishicat concept trained by Atallahw on the Atallahw/pishi2 dataset. This is a Stable Diffusion model fine-tuned on the pishicat concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of pishicat cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `cat` images for the animal theme. It makes images of my cat. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('Atallahw/mycatPishi') image = pipeline().images[0] image ```

c957a3f8885ad22e8822decec72f62d9

paarthmadan/Sentiment-Analysis-API

paarthmadan

null

14

0

transformers

0

null

false

mit

null

0

[]

false

true

490

false

## Sentiment Analysis API This is the deployment part of the project. ### Training: Run the Google Colab notebook (Runtime = "GPU") (https://colab.research.google.com/drive/1EuF5FDl1X8VnuOO5RxzmM0c9TbtQrVm9?usp=sharing) ### Fine Tuning 1) Increasing #epochs 2) Increasing BATCH_SIZE to 32 3) Changing Adam optimizer rate ### Usage 1) Clone te repository 2) Set up the conda environment with requirements.txt 3) In terminal, run the command- uvivorn sentiment_analyzer.api:app --reload

2367b2b537664ea54063a69a3496daf4

geevegeorge/ddpm-hkuoct-dr-256-200ep

geevegeorge

null

28

2

diffusers

0

null

false

apache-2.0

['en']

['imagefolder']

null

0

[]

false

true

1,215

false

# ddpm-hkuoct-dr-256-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-hkuoct-dr-256-200ep/tensorboard?#scalars)

27917508ba587335a481a204a3194a23

furusu/fate-diffusion

furusu

null

19

101

diffusers

6

text-to-image

false

openrail++

null

7

1

5

1

0

['stable-diffusion', 'text-to-image']

false

true

2,386

false

# fate-diffusion This model is resumed from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) for 10 epochs on 53k images of fate series filtered by [aesthetic score](https://github.com/waifu-diffusion/aesthetic)>=0.9, and then 10 epoch on datasets added 8k images of closing up on face. Caption is danbooru style tags inferenced by [wd1-4 tagger](https://github.com/toriato/stable-diffusion-webui-wd14-tagger). # Usage almostly same as original model except model_id = "furusu/fate-diffusion". ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` ```python import torch from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "furusu/fate-diffusion" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "artoria pendragon,fate,standing,armor,gauntlets,hair ribbon,green eyes,puffy sleeves,skirt" negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry" image = pipe(prompt, height=768, width=768,negative_prompt=negative_prompt).images[0] image.save("image.png") ``` You can also use it on webui by putting [fate-diffusion.ckpt](fate-diffusion.ckpt) and [fate-diffusion.yaml](fate-diffusion.yaml) into models/Stable-Diffusion. See [this link](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20). # Generation examples with negative prompt:lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry artoria pendragon,fate,solo,standing,armor,gauntlets,hair ribbon,green eyes,puffy sleeves,skirt,blush ![image](artoria.png) nero claudius,bride,fate,sitting,bridal veil,((blush)),zipper,cleavage,white gloves,leotard,thighhighs,flower,belt,green eyes ![image](nero_bride1.png) 1girl,highres,solo,looking at viewer,blonde hair,twintails,maid,maid headdress,skirt,thighhighs,red eyes,blush ![image](twintailsmaid.png)

2859be6bb024a7e4637a01fa29529c43

jonas/roberta-base-finetuned-sdg

jonas

roberta

11

66

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,637

false

# roberta-base-finetuned-sdg This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4993 - Acc: 0.9024 ## 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4679 | 1.0 | 254 | 0.3660 | 0.8963 | | 0.3578 | 2.0 | 508 | 0.3689 | 0.9019 | | 0.2739 | 3.0 | 762 | 0.3284 | 0.9035 | | 0.1841 | 4.0 | 1016 | 0.3763 | 0.9019 | | 0.1127 | 5.0 | 1270 | 0.4174 | 0.9024 | | 0.0822 | 6.0 | 1524 | 0.4523 | 0.9013 | | 0.0329 | 7.0 | 1778 | 0.4829 | 0.9030 | | 0.0157 | 8.0 | 2032 | 0.4993 | 0.9024 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0a0+8a1a93a - Datasets 2.4.0 - Tokenizers 0.12.1

fb6b4647abff47d297878cfae257acae

jonatasgrosman/exp_w2v2t_th_unispeech-sat_s515

jonatasgrosman

unispeech-sat

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['th']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'th']

false

true

463

false

# exp_w2v2t_th_unispeech-sat_s515 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

b7ac06ee7e03aace16bc66333a2a0fbc

anas-awadalla/roberta-base-few-shot-k-16-finetuned-squad-seed-8

anas-awadalla

roberta

17

5

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

985

false

# roberta-base-few-shot-k-16-finetuned-squad-seed-8 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. ## 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: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

4521666aed61de928f4c189dd9ce0009

GyuBeen/distilbert-base-uncased-finetuned-squad

GyuBeen

distilbert

14

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,278

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1543 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.2119 | 1.0 | 5533 | 1.1532 | | 0.9427 | 2.0 | 11066 | 1.1100 | | 0.7477 | 3.0 | 16599 | 1.1543 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

fe65f9fc40b7ad23a6c692ab4d1a4725

nandysoham/Warsaw_Pact-clustered

nandysoham

distilbert

8

0

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,861

false

# nandysoham/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0792 - Train End Logits Accuracy: 0.9722 - Train Start Logits Accuracy: 0.9861 - Validation Loss: 1.0233 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 0 ## 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: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.0792 | 0.9722 | 0.9861 | 1.0233 | 1.0 | 0.0 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

c7e0d1e5c77063d44498645a4d5fdd71

takizawa/distilbert-base-uncased-finetuned-emotion

takizawa

distilbert

12

4

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,344

false

# 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: 0.2251 - Accuracy: 0.925 - F1: 0.9250 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8481 | 1.0 | 250 | 0.3248 | 0.907 | 0.9028 | | 0.2595 | 2.0 | 500 | 0.2251 | 0.925 | 0.9250 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

f47518141123d78b9a0431bda88170ed

jonatasgrosman/exp_w2v2t_th_vp-it_s334

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['th']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'th']

false

true

469

false

# exp_w2v2t_th_vp-it_s334 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

4ac71945090ba1f5b01e86c6176ce628

rti-international/rota

rti-international

roberta

19

10

transformers

2

text-classification

true

false

true

apache-2.0

['en']

null

0

[]

false

true

12,988

false

# ROTA ## Rapid Offense Text Autocoder [![HuggingFace Models](https://img.shields.io/badge/%F0%9F%A4%97%20models-2021.05.18.15-blue)](https://huggingface.co/rti-international/rota) [![HuggingFace Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20spaces-2021.05.18.15-blue)](https://huggingface.co/spaces/rti-international/rota-app) [![GitHub Model Release](https://img.shields.io/github/v/release/RTIInternational/rota?logo=github)](https://github.com/RTIInternational/rota) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4770492.svg)](https://doi.org/10.5281/zenodo.4770492) ROTA Application hosted on Hugging Face Spaces: https://huggingface.co/spaces/rti-international/rota-app Criminal justice research often requires conversion of free-text offense descriptions into overall charge categories to aid analysis. For example, the free-text offense of "eluding a police vehicle" would be coded to a charge category of "Obstruction - Law Enforcement". Since free-text offense descriptions aren't standardized and often need to be categorized in large volumes, this can result in a manual and time intensive process for researchers. ROTA is a machine learning model for converting offense text into offense codes. Currently ROTA predicts the *Charge Category* of a given offense text. A *charge category* is one of the headings for offense codes in the [2009 NCRP Codebook: Appendix F](https://www.icpsr.umich.edu/web/NACJD/studies/30799/datadocumentation#). The model was trained on [publicly available data](https://web.archive.org/web/20201021001250/https://www.icpsr.umich.edu/web/pages/NACJD/guides/ncrp.html) from a crosswalk containing offenses from all 50 states combined with three additional hand-labeled offense text datasets. <details> <summary>Charge Category Example</summary> <img src="https://i.ibb.co/xLsrzmV/charge-category-example.png" width="500"> </details> ### Data Preprocessing The input text is standardized through a series of preprocessing steps. The text is first passed through a sequence of 500+ case-insensitive regular expressions that identify common misspellings and abbreviations and expand the text to a more full, correct English text. Some data-specific prefixes and suffixes are then removed from the text -- e.g. some states included a statute as a part of the text. Finally, punctuation (excluding dollar signs) are removed from the input, multiple spaces between words are removed, and the text is lowercased. ## Cross-Validation Performance This model was evaluated using 3-fold cross validation. Except where noted, numbers presented below are the mean value across the 3 folds. The model in this repository is trained on all available data. Because of this, you can typically expect production performance to be (unknowably) better than the numbers presented below. ### Overall Metrics | Metric | Value | | -------- | ----- | | Accuracy | 0.934 | | MCC | 0.931 | | Metric | precision | recall | f1-score | | --------- | --------- | ------ | -------- | | macro avg | 0.811 | 0.786 | 0.794 | *Note*: These are the average of the values *per fold*, so *macro avg* is the average of the macro average of all categories per fold. ### Per-Category Metrics | Category | precision | recall | f1-score | support | | ------------------------------------------------------ | --------- | ------ | -------- | ------- | | AGGRAVATED ASSAULT | 0.954 | 0.954 | 0.954 | 4085 | | ARMED ROBBERY | 0.961 | 0.955 | 0.958 | 1021 | | ARSON | 0.946 | 0.954 | 0.95 | 344 | | ASSAULTING PUBLIC OFFICER | 0.914 | 0.905 | 0.909 | 588 | | AUTO THEFT | 0.962 | 0.962 | 0.962 | 1660 | | BLACKMAIL/EXTORTION/INTIMIDATION | 0.872 | 0.871 | 0.872 | 627 | | BRIBERY AND CONFLICT OF INTEREST | 0.784 | 0.796 | 0.79 | 216 | | BURGLARY | 0.979 | 0.981 | 0.98 | 2214 | | CHILD ABUSE | 0.805 | 0.78 | 0.792 | 139 | | COCAINE OR CRACK VIOLATION OFFENSE UNSPECIFIED | 0.827 | 0.815 | 0.821 | 47 | | COMMERCIALIZED VICE | 0.818 | 0.788 | 0.802 | 666 | | CONTEMPT OF COURT | 0.982 | 0.987 | 0.984 | 2952 | | CONTRIBUTING TO DELINQUENCY OF A MINOR | 0.544 | 0.333 | 0.392 | 50 | | CONTROLLED SUBSTANCE - OFFENSE UNSPECIFIED | 0.864 | 0.791 | 0.826 | 280 | | COUNTERFEITING (FEDERAL ONLY) | 0 | 0 | 0 | 2 | | DESTRUCTION OF PROPERTY | 0.97 | 0.968 | 0.969 | 2560 | | DRIVING UNDER INFLUENCE - DRUGS | 0.567 | 0.603 | 0.581 | 34 | | DRIVING UNDER THE INFLUENCE | 0.951 | 0.946 | 0.949 | 2195 | | DRIVING WHILE INTOXICATED | 0.986 | 0.981 | 0.984 | 2391 | | DRUG OFFENSES - VIOLATION/DRUG UNSPECIFIED | 0.903 | 0.911 | 0.907 | 3100 | | DRUNKENNESS/VAGRANCY/DISORDERLY CONDUCT | 0.856 | 0.861 | 0.858 | 380 | | EMBEZZLEMENT | 0.865 | 0.759 | 0.809 | 100 | | EMBEZZLEMENT (FEDERAL ONLY) | 0 | 0 | 0 | 1 | | ESCAPE FROM CUSTODY | 0.988 | 0.991 | 0.989 | 4035 | | FAMILY RELATED OFFENSES | 0.739 | 0.773 | 0.755 | 442 | | FELONY - UNSPECIFIED | 0.692 | 0.735 | 0.712 | 122 | | FLIGHT TO AVOID PROSECUTION | 0.46 | 0.407 | 0.425 | 38 | | FORCIBLE SODOMY | 0.82 | 0.8 | 0.809 | 76 | | FORGERY (FEDERAL ONLY) | 0 | 0 | 0 | 2 | | FORGERY/FRAUD | 0.911 | 0.928 | 0.919 | 4687 | | FRAUD (FEDERAL ONLY) | 0 | 0 | 0 | 2 | | GRAND LARCENY - THEFT OVER $200 | 0.957 | 0.973 | 0.965 | 2412 | | HABITUAL OFFENDER | 0.742 | 0.627 | 0.679 | 53 | | HEROIN VIOLATION - OFFENSE UNSPECIFIED | 0.879 | 0.811 | 0.843 | 24 | | HIT AND RUN DRIVING | 0.922 | 0.94 | 0.931 | 303 | | HIT/RUN DRIVING - PROPERTY DAMAGE | 0.929 | 0.918 | 0.923 | 362 | | IMMIGRATION VIOLATIONS | 0.84 | 0.609 | 0.697 | 19 | | INVASION OF PRIVACY | 0.927 | 0.923 | 0.925 | 1235 | | JUVENILE OFFENSES | 0.928 | 0.866 | 0.895 | 144 | | KIDNAPPING | 0.937 | 0.93 | 0.933 | 553 | | LARCENY/THEFT - VALUE UNKNOWN | 0.955 | 0.945 | 0.95 | 3175 | | LEWD ACT WITH CHILDREN | 0.775 | 0.85 | 0.811 | 596 | | LIQUOR LAW VIOLATIONS | 0.741 | 0.768 | 0.755 | 214 | | MANSLAUGHTER - NON-VEHICULAR | 0.626 | 0.802 | 0.701 | 139 | | MANSLAUGHTER - VEHICULAR | 0.79 | 0.853 | 0.819 | 117 | | MARIJUANA/HASHISH VIOLATION - OFFENSE UNSPECIFIED | 0.741 | 0.662 | 0.699 | 62 | | MISDEMEANOR UNSPECIFIED | 0.63 | 0.243 | 0.347 | 57 | | MORALS/DECENCY - OFFENSE | 0.774 | 0.764 | 0.769 | 412 | | MURDER | 0.965 | 0.915 | 0.939 | 621 | | OBSTRUCTION - LAW ENFORCEMENT | 0.939 | 0.947 | 0.943 | 4220 | | OFFENSES AGAINST COURTS, LEGISLATURES, AND COMMISSIONS | 0.881 | 0.895 | 0.888 | 1965 | | PAROLE VIOLATION | 0.97 | 0.953 | 0.962 | 946 | | PETTY LARCENY - THEFT UNDER $200 | 0.965 | 0.761 | 0.85 | 139 | | POSSESSION/USE - COCAINE OR CRACK | 0.893 | 0.928 | 0.908 | 68 | | POSSESSION/USE - DRUG UNSPECIFIED | 0.624 | 0.535 | 0.572 | 189 | | POSSESSION/USE - HEROIN | 0.884 | 0.852 | 0.866 | 25 | | POSSESSION/USE - MARIJUANA/HASHISH | 0.977 | 0.97 | 0.973 | 556 | | POSSESSION/USE - OTHER CONTROLLED SUBSTANCES | 0.975 | 0.965 | 0.97 | 3271 | | PROBATION VIOLATION | 0.963 | 0.953 | 0.958 | 1158 | | PROPERTY OFFENSES - OTHER | 0.901 | 0.87 | 0.885 | 446 | | PUBLIC ORDER OFFENSES - OTHER | 0.7 | 0.721 | 0.71 | 1871 | | RACKETEERING/EXTORTION (FEDERAL ONLY) | 0 | 0 | 0 | 2 | | RAPE - FORCE | 0.842 | 0.873 | 0.857 | 641 | | RAPE - STATUTORY - NO FORCE | 0.707 | 0.55 | 0.611 | 140 | | REGULATORY OFFENSES (FEDERAL ONLY) | 0.847 | 0.567 | 0.674 | 70 | | RIOTING | 0.784 | 0.605 | 0.68 | 119 | | SEXUAL ASSAULT - OTHER | 0.836 | 0.836 | 0.836 | 971 | | SIMPLE ASSAULT | 0.976 | 0.967 | 0.972 | 4577 | | STOLEN PROPERTY - RECEIVING | 0.959 | 0.957 | 0.958 | 1193 | | STOLEN PROPERTY - TRAFFICKING | 0.902 | 0.888 | 0.895 | 491 | | TAX LAW (FEDERAL ONLY) | 0.373 | 0.233 | 0.286 | 30 | | TRAFFIC OFFENSES - MINOR | 0.974 | 0.977 | 0.976 | 8699 | | TRAFFICKING - COCAINE OR CRACK | 0.896 | 0.951 | 0.922 | 185 | | TRAFFICKING - DRUG UNSPECIFIED | 0.709 | 0.795 | 0.749 | 516 | | TRAFFICKING - HEROIN | 0.871 | 0.92 | 0.894 | 54 | | TRAFFICKING - OTHER CONTROLLED SUBSTANCES | 0.963 | 0.954 | 0.959 | 2832 | | TRAFFICKING MARIJUANA/HASHISH | 0.921 | 0.943 | 0.932 | 255 | | TRESPASSING | 0.974 | 0.98 | 0.977 | 1916 | | UNARMED ROBBERY | 0.941 | 0.939 | 0.94 | 377 | | UNAUTHORIZED USE OF VEHICLE | 0.94 | 0.908 | 0.924 | 304 | | UNSPECIFIED HOMICIDE | 0.61 | 0.554 | 0.577 | 60 | | VIOLENT OFFENSES - OTHER | 0.827 | 0.817 | 0.822 | 606 | | VOLUNTARY/NONNEGLIGENT MANSLAUGHTER | 0.619 | 0.513 | 0.542 | 54 | | WEAPON OFFENSE | 0.943 | 0.949 | 0.946 | 2466 | *Note: `support` is the average number of observations predicted on per fold, so the total number of observations per class is roughly 3x `support`.* ### Using Confidence Scores If we interpret the classification probability as a confidence score, we can use it to filter out predictions that the model isn't as confident about. We applied this process in 3-fold cross validation. The numbers presented below indicate how much of the prediction data is retained given a confidence score cutoff of `p`. We present the overall accuracy and MCC metrics as if the model was only evaluated on this subset of confident predictions. | | cutoff | percent retained | mcc | acc | | --- | ------ | ---------------- | ----- | ----- | | 0 | 0.85 | 0.952 | 0.96 | 0.961 | | 1 | 0.9 | 0.943 | 0.964 | 0.965 | | 2 | 0.95 | 0.928 | 0.97 | 0.971 | | 3 | 0.975 | 0.912 | 0.975 | 0.976 | | 4 | 0.99 | 0.886 | 0.982 | 0.983 | | 5 | 0.999 | 0.733 | 0.995 | 0.996 |

d5f8acff019043ff25eb3b03395213b0

Yanis/biomedical-ner-all-finetuned-ner

Yanis

distilbert

18

19

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

10,463

false

# biomedical-ner-all-finetuned-ner This model is a fine-tuned version of [d4data/biomedical-ner-all](https://huggingface.co/d4data/biomedical-ner-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 4 | 0.1905 | 0.7660 | 0.8480 | 0.8049 | 0.9447 | | No log | 2.0 | 8 | 0.1689 | 0.7619 | 0.8565 | 0.8065 | 0.9482 | | No log | 3.0 | 12 | 0.1535 | 0.7950 | 0.8801 | 0.8354 | 0.9552 | | No log | 4.0 | 16 | 0.1337 | 0.816 | 0.8737 | 0.8438 | 0.9598 | | No log | 5.0 | 20 | 0.1206 | 0.8191 | 0.9015 | 0.8583 | 0.9636 | | No log | 6.0 | 24 | 0.1047 | 0.8285 | 0.9101 | 0.8673 | 0.9689 | | No log | 7.0 | 28 | 0.0943 | 0.8509 | 0.9165 | 0.8825 | 0.9703 | | No log | 8.0 | 32 | 0.0848 | 0.8495 | 0.9186 | 0.8827 | 0.9734 | | No log | 9.0 | 36 | 0.0756 | 0.8732 | 0.9293 | 0.9004 | 0.9780 | | No log | 10.0 | 40 | 0.0671 | 0.8884 | 0.9379 | 0.9125 | 0.9825 | | No log | 11.0 | 44 | 0.0625 | 0.8912 | 0.9293 | 0.9099 | 0.9836 | | No log | 12.0 | 48 | 0.0548 | 0.9048 | 0.9358 | 0.92 | 0.9857 | | No log | 13.0 | 52 | 0.0492 | 0.9129 | 0.9422 | 0.9273 | 0.9881 | | No log | 14.0 | 56 | 0.0466 | 0.9187 | 0.9443 | 0.9314 | 0.9885 | | No log | 15.0 | 60 | 0.0400 | 0.9308 | 0.9507 | 0.9407 | 0.9902 | | No log | 16.0 | 64 | 0.0369 | 0.9414 | 0.9636 | 0.9524 | 0.9920 | | No log | 17.0 | 68 | 0.0342 | 0.9356 | 0.9636 | 0.9494 | 0.9920 | | No log | 18.0 | 72 | 0.0300 | 0.9514 | 0.9636 | 0.9574 | 0.9937 | | No log | 19.0 | 76 | 0.0284 | 0.9558 | 0.9722 | 0.9639 | 0.9941 | | No log | 20.0 | 80 | 0.0270 | 0.9580 | 0.9764 | 0.9671 | 0.9944 | | No log | 21.0 | 84 | 0.0234 | 0.9621 | 0.9786 | 0.9703 | 0.9948 | | No log | 22.0 | 88 | 0.0215 | 0.9662 | 0.9807 | 0.9734 | 0.9955 | | No log | 23.0 | 92 | 0.0199 | 0.9662 | 0.9807 | 0.9734 | 0.9955 | | No log | 24.0 | 96 | 0.0179 | 0.9725 | 0.9829 | 0.9776 | 0.9955 | | No log | 25.0 | 100 | 0.0164 | 0.9851 | 0.9893 | 0.9872 | 0.9965 | | No log | 26.0 | 104 | 0.0158 | 0.9851 | 0.9914 | 0.9883 | 0.9969 | | No log | 27.0 | 108 | 0.0145 | 0.9809 | 0.9914 | 0.9862 | 0.9979 | | No log | 28.0 | 112 | 0.0130 | 0.9809 | 0.9914 | 0.9862 | 0.9983 | | No log | 29.0 | 116 | 0.0122 | 0.9809 | 0.9914 | 0.9862 | 0.9983 | | No log | 30.0 | 120 | 0.0114 | 0.9872 | 0.9936 | 0.9904 | 0.9986 | | No log | 31.0 | 124 | 0.0104 | 0.9872 | 0.9936 | 0.9904 | 0.9990 | | No log | 32.0 | 128 | 0.0098 | 0.9830 | 0.9914 | 0.9872 | 0.9986 | | No log | 33.0 | 132 | 0.0094 | 0.9830 | 0.9914 | 0.9872 | 0.9986 | | No log | 34.0 | 136 | 0.0091 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 35.0 | 140 | 0.0085 | 0.9915 | 0.9957 | 0.9936 | 0.9990 | | No log | 36.0 | 144 | 0.0080 | 0.9915 | 0.9957 | 0.9936 | 0.9990 | | No log | 37.0 | 148 | 0.0073 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 38.0 | 152 | 0.0069 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 39.0 | 156 | 0.0067 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 40.0 | 160 | 0.0063 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 41.0 | 164 | 0.0059 | 0.9915 | 0.9957 | 0.9936 | 0.9993 | | No log | 42.0 | 168 | 0.0056 | 0.9957 | 0.9979 | 0.9968 | 0.9997 | | No log | 43.0 | 172 | 0.0053 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 44.0 | 176 | 0.0050 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 45.0 | 180 | 0.0049 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 46.0 | 184 | 0.0049 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 47.0 | 188 | 0.0046 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 48.0 | 192 | 0.0043 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 49.0 | 196 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 50.0 | 200 | 0.0041 | 0.9957 | 0.9979 | 0.9968 | 0.9997 | | No log | 51.0 | 204 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 52.0 | 208 | 0.0038 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 53.0 | 212 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 54.0 | 216 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 55.0 | 220 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 56.0 | 224 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 57.0 | 228 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 58.0 | 232 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 59.0 | 236 | 0.0031 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 60.0 | 240 | 0.0030 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 61.0 | 244 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 62.0 | 248 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 63.0 | 252 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 64.0 | 256 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 65.0 | 260 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 66.0 | 264 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 67.0 | 268 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 68.0 | 272 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 69.0 | 276 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 70.0 | 280 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 71.0 | 284 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 72.0 | 288 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 73.0 | 292 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 74.0 | 296 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 75.0 | 300 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 76.0 | 304 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 77.0 | 308 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 78.0 | 312 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 79.0 | 316 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 80.0 | 320 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 81.0 | 324 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 82.0 | 328 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 83.0 | 332 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 84.0 | 336 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 85.0 | 340 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 86.0 | 344 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 87.0 | 348 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 88.0 | 352 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 89.0 | 356 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 90.0 | 360 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 91.0 | 364 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 92.0 | 368 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 93.0 | 372 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 94.0 | 376 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 95.0 | 380 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 96.0 | 384 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 97.0 | 388 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 98.0 | 392 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 99.0 | 396 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 100.0 | 400 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

3b5d9e51c124f9291c20e9836d1b9522

vumichien/AnimeGANv2_Shinkai

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv2']

false

true

682

false

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv2_Shinkai Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

83fa3f820c591b796f793f3e44ae3000

MultiBertGunjanPatrick/multiberts-seed-3-120k

MultiBertGunjanPatrick

bert

7

4

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-3']

false

true

6,483

false

# MultiBERTs Seed 3 Checkpoint 120k (uncased) Seed 3 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-3](https://hf.co/multberts-seed-3). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-120k') model = BertModel.from_pretrained("multiberts-seed-3-120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

6b0be0896205bf993b3e015693e76432

agnesluhtaru/whisper-small-et-ERR2020

agnesluhtaru

whisper

30

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['whisper-event', 'generated_from_trainer']

true

1,856

false

# whisper-small-et-ERR2020 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5321 - Wer: 22.8462 ## 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: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.3387 | 0.1 | 1000 | 0.5383 | 33.8216 | | 0.1393 | 0.2 | 2000 | 0.4897 | 27.7546 | | 0.0982 | 0.3 | 3000 | 0.5477 | 26.7815 | | 0.0912 | 1.02 | 4000 | 0.5195 | 24.8816 | | 0.0811 | 1.12 | 5000 | 0.5373 | 25.9282 | | 0.0649 | 1.22 | 6000 | 0.5422 | 23.7285 | | 0.0618 | 1.32 | 7000 | 0.5504 | 23.5179 | | 0.0558 | 2.03 | 8000 | 0.5321 | 22.8462 | | 0.0452 | 2.13 | 9000 | 0.5543 | 23.5813 | | 0.0462 | 2.23 | 10000 | 0.5424 | 22.8830 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+rocm5.1.1 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

fa5a3dafa9322d7e27e3eb2b1a75fb04

spacy/hr_core_news_lg

spacy

null

32

1

spacy

0

token-classification

false

cc-by-sa-4.0

['hr']

null

0

['spacy', 'token-classification']

false

true

42,190

false

### Details: https://spacy.io/models/hr#hr_core_news_lg Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `hr_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | floret (200000, 300) | | **Sources** | [Training corpus hr500k 1.0](http://hdl.handle.net/11356/1183) (Ljubešić, Nikola ; Agić, Željko ; Klubička, Filip ; Batanović, Vuk and Erjavec, Tomaž)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (1518 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsdy`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmsany`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsgy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdn`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsgn`, `Aspnsgy`, `Aspnsln`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pl`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, _(truncated: full list in pipeline meta)_ | | **`morphologizer`** | `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Ins\|POS=ADP`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Degree=Pos\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=PART`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=CCONJ`, `Case=Gen\|POS=ADP`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `NumType=Ord\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Int,Rel`, `Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=X`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=NOUN`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=SPACE`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|POS=ADP`, `Degree=Sup\|POS=ADV`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `POS=ADV\|Tense=Pres\|VerbForm=Conv`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `NumType=Mult\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=ADV\|Tense=Past\|VerbForm=Conv`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Degree=Pos\|POS=ADV\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Gen\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Mult\|POS=SYM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, _(truncated: full list in pipeline meta)_ | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl:pv`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `DERIV_PER`, `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.89 | | `TOKEN_P` | 97.28 | | `TOKEN_R` | 98.71 | | `TOKEN_F` | 97.99 | | `TAG_ACC` | 92.17 | | `POS_ACC` | 97.59 | | `MORPH_ACC` | 92.68 | | `MORPH_MICRO_P` | 96.20 | | `MORPH_MICRO_R` | 95.88 | | `MORPH_MICRO_F` | 96.04 | | `SENTS_P` | 95.59 | | `SENTS_R` | 93.53 | | `SENTS_F` | 94.55 | | `DEP_UAS` | 86.62 | | `DEP_LAS` | 80.16 | | `LEMMA_ACC` | 92.88 | | `ENTS_P` | 83.09 | | `ENTS_R` | 82.95 | | `ENTS_F` | 83.02 |

de5277e30a8951a68b754ab4a207e567

madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1

madlag

bert

93

8

transformers

0

question-answering

true

false

mit

['en']

['squad']

null

0

['question-answering']

false

true

3,809

false

## BERT-base uncased model fine-tuned on SQuAD v1 This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original weights. The model contains **28.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method). With a simple resizing of the linear matrices it ran **1.16x as fast as bert-base-uncased** on the evaluation. This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix. <div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/density_info.js" id="c60d09ec-81ff-4d6f-b616-c3ef09b2175d"></script></div> In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.39**. ## Fine-Pruning details This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-base-uncased) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) This model is case-insensitive: it does not make a difference between english and English. A side-effect of the block pruning is that some of the attention heads are completely removed: 22 heads were removed on a total of 144 (15.3%). Here is a detailed view on how the remaining heads are distributed in the network after pruning. <div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/pruning_info.js" id="55528c8b-d5f5-46a5-a35a-dad93725f7e5"></script></div> ## Details of the SQuAD1.1 dataset | Dataset | Split | # samples | | -------- | ----- | --------- | | SQuAD1.1 | train | 90.6K | | SQuAD1.1 | eval | 11.1k | ### Fine-tuning - Python: `3.8.5` - Machine specs: ```CPU: Intel(R) Core(TM) i7-6700K CPU Memory: 64 GiB GPUs: 1 GeForce GTX 3090, with 24GiB memory GPU driver: 455.23.05, CUDA: 11.1 ``` ### Results **Pytorch model file size**: `398MB` (original BERT: `420MB`) | Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation | | ------ | --------- | --------- | --------- | | **EM** | **80.94** | **80.8** | **+0.14**| | **F1** | **88.11** | **88.5** | **-0.39**| ## Example Usage Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns. `pip install nn_pruning` Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded. ```python from transformers import pipeline from nn_pruning.inference_model_patcher import optimize_model qa_pipeline = pipeline( "question-answering", model="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1", tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1" ) print("bert-base-uncased parameters: 152.0M") print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M") qa_pipeline.model = optimize_model(qa_pipeline.model, "dense") print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M") predictions = qa_pipeline({ 'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.", 'question': "Who is Frederic Chopin?", }) print("Predictions", predictions) ```

fbfa305913d05390e9d355722f4b00eb

harveyagraphcore/roberta-base-finetuned-squad

harveyagraphcore

roberta

12

3

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

1,103

false

# roberta-base-finetuned-squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 3 - training precision: Mixed Precision ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cpu - Datasets 2.1.0 - Tokenizers 0.12.1

e3b84a4e2bd5bf0191ebb290f9699898

dhlanm/distilbert-base-uncased-finetune

dhlanm

distilbert

14

11

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,519

false

# distilbert-base-uncased-finetune This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1315 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9715 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 48 | 0.1349 | 0.0 | 0.0 | 0.0 | 0.9715 | | No log | 2.0 | 96 | 0.1318 | 0.0 | 0.0 | 0.0 | 0.9715 | | No log | 3.0 | 144 | 0.1315 | 0.0 | 0.0 | 0.0 | 0.9715 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

1d98f8808752ff699e31ec0f5650b921

ML-Projects-Kiel/tweetyface

ML-Projects-Kiel

gpt2

9

3

transformers

2

text-generation

true

false

apache-2.0

['en']

['ML-Projects-Kiel/tweetyface']

null

0

['gpt2']

false

true

2,807

false

# Tweety Face Finetuned language model based on [GPT-2](https://huggingface.co/gpt2) to generate Tweets in a users style. ## Model description Tweety Face is a transformer model finetuned using GTP-2 and Tweets from various Twitter users. It was created to generate a Twitter Tweet for a given user similar to their specific writing style. It accepts a prompt for a user and completes the text. This finetuned model uses the **smallest** version of GPT-2, with 124M parameters. ## Intended uses & limitations This model was created to experiment with prompt inputs and is not intended to create real Tweets. The generated text is not a real representation of the given users opinion, political affiliation, behaviour, etc. Do not use this model to impersonate a user. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='ML-Projects-Kiel/tweetyface') >>> set_seed(42) >>> generator("User: elonmusk\nTweet: Twitter is", max_length=30, num_return_sequences=5) [{'generated_text': 'User: elonmusk\nTweet: Twitter is more active than ever. Even though you can’t see your entire phone list, your'}, {'generated_text': 'User: elonmusk\nTweet: Twitter is just in a few hours until an announcement which has been approved by President. This should be a'}, {'generated_text': 'User: elonmusk\nTweet: Twitter is currently down to a minimum of 13 tweets per day, a decline that was significantly worse than Twitter'}, {'generated_text': 'User: elonmusk\nTweet: Twitter is a great investment to us. Will go above his legal fees to join Twitter in many countries,'}, {'generated_text': 'User: elonmusk\nTweet: Twitter is not doing something like this – they are not using Twitter to give out their content – other than'}] ``` ## Training data The training data used for this model has been released as a dataset one can browse [here](https://huggingface.co/ML-Projects-Kiel/tweetyface). The raw data can be found in our [Github Repository](https://github.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers). The raw data can be found in two versions. All data on the develop branch is used in a [debugging dataset](https://huggingface.co/datasets/ML-Projects-Kiel/tweetyface_debug). All data in the qa branch is used in the final dataset. ## Training procedure ### Preprocessing For training first all retweets (RT) have been removed. Next the newline characters "\n" have been replaced by white spaces and all URLs haven been replaced with the word URL. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters).

bc60ad0d1a49dd24865a33a56608ad42

kpriyanshu256/whisper-small-as-500-64-1e-05-bn

kpriyanshu256

whisper

15

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['as']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,442

false

# openai/whisper-small-Assamese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.3386 - Wer: 61.7578 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3132 | 0.3 | 150 | 1.4029 | 161.4149 | | 0.1888 | 1.08 | 300 | 1.3000 | 61.7217 | | 0.1358 | 1.38 | 450 | 1.3386 | 61.7578 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

19232b2057c8ccf82c2a89f7e924bde9

allenai/aspire-contextualsentence-multim-biomed

allenai

bert

7

5

transformers

0

feature-extraction

true

false

apache-2.0

['en']

null

1

0

1

0

[]

false

true

5,925

false

## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `tsAspire` and represents the papers proposed multi-vector model for fine-grained scientific document similarity. ## Model Card ### Model description This model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - the whole title and abstract are encoded with cross-attention in the encoder block before obtaining sentence embeddings. The model is trained by minimizing an Wasserstein/Earth Movers Distance between sentence vectors for a pair of documents - in the process also learning a sparse alignment between sentences in both documents. Test time behavior ranks documents based on the Wasserstein Distance between all sentences of documents or a set of query sentences and a candidate documents sentences. ### Training data The model is trained on pairs of co-cited papers with their sentences aligned by the co-citation context in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model, negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers. For example - the papers in brackets below are all co-cited and each pair of papers would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for fine-grained document similarity tasks in **biomedical** scientific text using multiple vectors per document. The model allows _multiple_ fine grained sentence-to-sentence similarities between documents. The model is well suited to an aspect conditional task formulation where a query might consist of sentence_s_ in a query document and candidates must be retrieved along the specified sentences. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as document or sentence level classification. Since the training data comes primarily from biomedical, performance on other domains may be poorer. ### How to use This model can be used via the `transformers` library, and some additional code to compute contextual sentence vectors and to make multiple matches using optimal transport. View example usage and sample document matches in the model github repo: [`examples/demo-contextualsentence-multim.ipynb`](https://github.com/allenai/aspire/blob/main/examples/demo-contextualsentence-multim.ipynb) ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. In using this model we rank documents by the Wasserstein distance between the query sentences and a candidates sentences. ### Evaluation results The released model `aspire-contextualsentence-multim-biomed` is compared against `allenai/specter`. `aspire-contextualsentence-multim-biomed`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-contextualsentence-multim-biomed` is the single best run among the 3 re-runs. | | TRECCOVID | TRECCOVID | RELISH | RELISH | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| | | MAP | NDCG%20 | MAP | NDCG%20 | | `specter` | 28.24 | 59.28 | 60.62 | 77.20 | | `aspire-contextualsentence-multim-biomed`<sup>*</sup> | 30.92 | 62.23 | 62.57 | 78.95 | | `aspire-contextualsentence-multim-biomed` | 31.25 | 62.99 | 62.24 | 78.65 | **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-contextualsentence-multim-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-multim-compsci): If you wanted to run on computer science papers and want to use a model trained to match _multiple_ sentences between documents. [`aspire-contextualsentence-singlem-biomed`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-biomed): If you wanted to run on biomedical papers and want to use a model trained to match _single_ sentences between documents. [`aspire-contextualsentence-singlem-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-compsci): If you wanted to run on computer science papers and want to use a model trained to match _single_ sentences between documents.

81176edaf8c7be8bfc44cee216b496d1

phailyoor/distilbert-base-uncased-finetuned-yahd-twval-hptune

phailyoor

distilbert

96

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,495

false

# distilbert-base-uncased-finetuned-yahd-twval-hptune This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3727 - Accuracy: 0.2039 ## 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: 6e-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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.1638 | 1.0 | 10106 | 2.1944 | 0.3646 | | 1.7982 | 2.0 | 20212 | 2.6390 | 0.3333 | | 1.3279 | 3.0 | 30318 | 3.1526 | 0.3095 | | 0.8637 | 4.0 | 40424 | 4.8368 | 0.2470 | | 0.5727 | 5.0 | 50530 | 6.3727 | 0.2039 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3

22c544e3255e5032375efee97c615a83

banjtheman/distilbert-base-uncased-helpful-amazon

banjtheman

distilbert

13

7

transformers

0

text-classification

true

false

apache-2.0

null

0

[]

false

true

976

false

## Overview This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is. The model was fine-tuned from the `distilbert-base-uncased` model ### Labels LABEL_0 - Not helpful LABEL_1 - Helpful ### How to use The following code shows how to make a prediction with this model ```python from transformers import ( AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, ) tokenizer = AutoTokenizer.from_pretrained("banjtheman/distilbert-base-uncased-helpful-amazon") model = AutoModelForSequenceClassification.from_pretrained( "banjtheman/distilbert-base-uncased-helpful-amazon" ) pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer) result = pipe("This was a Christmas gift for my grandson.") print(result) #[{'label': 'LABEL_0', 'score': 0.998775064945221}] # This is NOT A HELPFUL comment ```

fb18e55110327e677323d7d1040fae08

vinayak361/token_fine_tunned_flipkart_2_gl

vinayak361

distilbert

12

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,004

false

# token_fine_tunned_flipkart_2_gl This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0275 - Precision: 0.9888 - Recall: 0.9900 - F1: 0.9894 - Accuracy: 0.9924 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 136 | 0.1945 | 0.9023 | 0.9338 | 0.9178 | 0.9331 | | No log | 2.0 | 272 | 0.1232 | 0.9469 | 0.9572 | 0.9520 | 0.9658 | | No log | 3.0 | 408 | 0.0852 | 0.9595 | 0.9688 | 0.9641 | 0.9747 | | 0.2214 | 4.0 | 544 | 0.0603 | 0.9723 | 0.9760 | 0.9741 | 0.9831 | | 0.2214 | 5.0 | 680 | 0.0455 | 0.9770 | 0.9819 | 0.9794 | 0.9865 | | 0.2214 | 6.0 | 816 | 0.0357 | 0.9823 | 0.9863 | 0.9843 | 0.9887 | | 0.2214 | 7.0 | 952 | 0.0307 | 0.9869 | 0.9894 | 0.9882 | 0.9916 | | 0.0938 | 8.0 | 1088 | 0.0275 | 0.9888 | 0.9900 | 0.9894 | 0.9924 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

c17a1ee3f5136c37e741bbf0e9888150

aXhyra/emotion_trained_42

aXhyra

distilbert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,391

false

# emotion_trained_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9012 - F1: 0.7361 ## 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: 6.961635072722524e-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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6131 | 0.6955 | | No log | 2.0 | 408 | 0.5816 | 0.7297 | | 0.5148 | 3.0 | 612 | 0.8942 | 0.7199 | | 0.5148 | 4.0 | 816 | 0.9012 | 0.7361 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

6a2dc39ee0a164ecdd799bf20ff23a87

tensorspeech/tts-tacotron2-thorsten-ger

tensorspeech

null

5

0

tensorflowtts

0

text-to-speech

false

apache-2.0

['german']

['Thorsten']

null

0

['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel']

false

true

2,696

false

# Tacotron 2 with Guided Attention trained on Thorsten (Ger) This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Thorsten dataset (Ger). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). ## Install TensorFlowTTS First of all, please install TensorFlowTTS with the following command: ``` pip install TensorFlowTTS ``` ### Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger") tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger") text = "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht." input_ids = processor.text_to_sequence(text) decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), ) ``` #### Referencing Tacotron 2 ``` @article{DBLP:journals/corr/abs-1712-05884, author = {Jonathan Shen and Ruoming Pang and Ron J. Weiss and Mike Schuster and Navdeep Jaitly and Zongheng Yang and Zhifeng Chen and Yu Zhang and Yuxuan Wang and R. J. Skerry{-}Ryan and Rif A. Saurous and Yannis Agiomyrgiannakis and Yonghui Wu}, title = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions}, journal = {CoRR}, volume = {abs/1712.05884}, year = {2017}, url = {http://arxiv.org/abs/1712.05884}, archivePrefix = {arXiv}, eprint = {1712.05884}, timestamp = {Thu, 28 Nov 2019 08:59:52 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Referencing TensorFlowTTS ``` @misc{TFTTS, author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata, Trinh Le and Yunchao He}, title = {TensorflowTTS}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}}, } ```

b6173cec6b5752cad4d9ef5e1ac232c2

caffsean/t5-small-finetuned-keyword-to-text-generation

caffsean

t5

20

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,360

false

# t5-small-finetuned-keyword-to-text-generation This model is a fine-tuned version of [caffsean/t5-small-finetuned-keyword-to-text-generation](https://huggingface.co/caffsean/t5-small-finetuned-keyword-to-text-generation) on an unknown dataset. ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 188 | 3.8742 | 0.5567 | 0.0851 | 0.4968 | 0.4972 | 16.243 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

6ffa127a5d381ea6ca75c11fd23b0d73

EleutherAI/enformer-preview

EleutherAI

enformer

4

1

transformers

2

null

true

false

apache-2.0

null

0

[]

false

true

1,462

false

# Enformer Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer). This particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for 2 and a half days without augmentations and poisson loss. This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the [enformer-pytorch repository](https://github.com/lucidrains/enformer-pytorch). Disclaimer: The team releasing Enformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence. We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details. ### How to use Refer to the README of [enformer-pytorch](https://github.com/lucidrains/enformer-pytorch) regarding usage. ### Citation info ``` Avsec, Ž., Agarwal, V., Visentin, D. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021). https://doi.org/10.1038/s41592-021-01252-x ```

90d1ec271241cda95cc21ee75b9d9c01

anas-awadalla/bert-base-uncased-prefix-tuning-squad

anas-awadalla

null

21

0

null

0

null

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,039

false

# bert-base-uncased-prefix-tuning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## 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: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

2af3c5432edcdb93681c0177ba04b3c7

IMSyPP/hate_speech_slo

IMSyPP

bert

7

9

transformers

0

text-classification

true

false

mit

['sl']

null

0

[]

false

true

670

false

# Hate Speech Classifier for Social Media Content in Slovenian Language A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model. ## Tokenizer During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent

e3940c3f18e0f462c3faacdd21df9255

theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e43

theojolliffe

bart

13

1

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

5,912

false

# bart-cnn-pubmed-arxiv-pubmed-v3-e43 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0837 - Rouge1: 58.1526 - Rouge2: 46.0425 - Rougel: 49.5624 - Rougelsum: 56.9295 - Gen Len: 142.0 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 43 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2542 | 1.0 | 795 | 0.9354 | 51.4655 | 31.6464 | 34.2376 | 48.9765 | 141.963 | | 0.7019 | 2.0 | 1590 | 0.8119 | 53.3066 | 34.683 | 36.4262 | 50.907 | 142.0 | | 0.5251 | 3.0 | 2385 | 0.7839 | 52.4248 | 32.8685 | 36.0084 | 49.9957 | 142.0 | | 0.3449 | 4.0 | 3180 | 0.7673 | 52.716 | 34.7869 | 38.4201 | 50.8384 | 142.0 | | 0.2666 | 5.0 | 3975 | 0.7647 | 54.6433 | 37.1337 | 40.1459 | 52.4288 | 141.7778 | | 0.1805 | 6.0 | 4770 | 0.8400 | 53.5747 | 36.001 | 39.5984 | 51.1935 | 141.8148 | | 0.1413 | 7.0 | 5565 | 0.7925 | 53.9875 | 37.01 | 40.6532 | 51.9353 | 142.0 | | 0.113 | 8.0 | 6360 | 0.7665 | 56.395 | 41.5764 | 44.327 | 54.7845 | 142.0 | | 0.0907 | 9.0 | 7155 | 0.8442 | 55.1407 | 39.4113 | 43.0628 | 53.6503 | 142.0 | | 0.0824 | 10.0 | 7950 | 0.8469 | 55.7103 | 40.6761 | 43.3754 | 53.8227 | 142.0 | | 0.0639 | 11.0 | 8745 | 0.8892 | 56.0839 | 40.6204 | 43.2455 | 54.4412 | 142.0 | | 0.0504 | 12.0 | 9540 | 0.8613 | 56.9634 | 42.8236 | 45.4255 | 55.4026 | 142.0 | | 0.0447 | 13.0 | 10335 | 0.9341 | 57.7216 | 44.104 | 47.1429 | 56.4299 | 142.0 | | 0.0396 | 14.0 | 11130 | 0.9203 | 56.2073 | 42.9575 | 45.8068 | 54.8089 | 142.0 | | 0.036 | 15.0 | 11925 | 0.9253 | 58.5212 | 45.6047 | 49.1205 | 57.0551 | 142.0 | | 0.0302 | 16.0 | 12720 | 0.9187 | 58.8046 | 46.0106 | 48.0442 | 57.2799 | 142.0 | | 0.0261 | 17.0 | 13515 | 0.9578 | 57.3405 | 43.8227 | 46.6317 | 55.7836 | 142.0 | | 0.0231 | 18.0 | 14310 | 0.9578 | 57.7604 | 44.6164 | 47.8902 | 56.2309 | 141.8148 | | 0.0198 | 19.0 | 15105 | 0.9662 | 57.774 | 44.6407 | 47.5489 | 56.1936 | 142.0 | | 0.0165 | 20.0 | 15900 | 0.9509 | 59.6297 | 46.5076 | 48.3507 | 58.083 | 142.0 | | 0.0145 | 21.0 | 16695 | 0.9915 | 58.2245 | 45.1804 | 48.1191 | 56.889 | 142.0 | | 0.0128 | 22.0 | 17490 | 0.9945 | 58.2646 | 46.2782 | 49.4411 | 56.992 | 142.0 | | 0.0129 | 23.0 | 18285 | 1.0069 | 57.0055 | 44.1866 | 46.9101 | 55.5056 | 141.9444 | | 0.0116 | 24.0 | 19080 | 0.9967 | 58.1091 | 45.5303 | 48.2208 | 56.4496 | 142.0 | | 0.0093 | 25.0 | 19875 | 1.0188 | 56.59 | 43.677 | 45.8956 | 55.0954 | 142.0 | | 0.008 | 26.0 | 20670 | 0.9976 | 58.5408 | 46.7019 | 48.9235 | 57.2562 | 142.0 | | 0.0077 | 27.0 | 21465 | 1.0123 | 57.7909 | 45.7619 | 48.3412 | 56.3796 | 142.0 | | 0.0075 | 28.0 | 22260 | 1.0258 | 58.1694 | 45.03 | 48.282 | 56.7303 | 142.0 | | 0.0056 | 29.0 | 23055 | 1.0100 | 58.0406 | 45.37 | 48.0125 | 56.5288 | 142.0 | | 0.0049 | 30.0 | 23850 | 1.0235 | 56.419 | 43.248 | 46.3448 | 54.8467 | 142.0 | | 0.0042 | 31.0 | 24645 | 1.0395 | 57.7232 | 45.6305 | 48.4531 | 56.3343 | 141.9444 | | 0.0034 | 32.0 | 25440 | 1.0605 | 58.9049 | 46.8049 | 49.9103 | 57.6751 | 141.5 | | 0.0032 | 33.0 | 26235 | 1.0362 | 57.8681 | 45.9028 | 48.8624 | 56.5616 | 141.8704 | | 0.0025 | 34.0 | 27030 | 1.0521 | 58.8985 | 46.8547 | 49.8485 | 57.4249 | 142.0 | | 0.0021 | 35.0 | 27825 | 1.0639 | 58.9324 | 46.656 | 49.1907 | 57.4836 | 142.0 | | 0.0023 | 36.0 | 28620 | 1.0624 | 58.5734 | 46.6774 | 49.6377 | 57.3825 | 142.0 | | 0.0019 | 37.0 | 29415 | 1.0636 | 58.9899 | 46.8217 | 49.4829 | 57.8683 | 142.0 | | 0.0018 | 38.0 | 30210 | 1.0640 | 58.793 | 46.7964 | 49.7845 | 57.6379 | 142.0 | | 0.0013 | 39.0 | 31005 | 1.0692 | 57.7124 | 45.5948 | 49.0482 | 56.4246 | 142.0 | | 0.0012 | 40.0 | 31800 | 1.0746 | 58.1789 | 46.458 | 49.547 | 57.1007 | 141.6296 | | 0.0008 | 41.0 | 32595 | 1.0815 | 57.7392 | 45.6404 | 48.4845 | 56.6464 | 142.0 | | 0.0009 | 42.0 | 33390 | 1.0853 | 58.317 | 46.2661 | 49.0466 | 57.0971 | 142.0 | | 0.0005 | 43.0 | 34185 | 1.0837 | 58.1526 | 46.0425 | 49.5624 | 56.9295 | 142.0 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

41638cb8f265b4d6f9864389d6cde889

dpkmnit/bert-finetuned-squad

dpkmnit

bert

8

4

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,289

false

# dpkmnit/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7048 - Epoch: 1 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 66549, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.2092 | 0 | | 0.7048 | 1 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.1 - Datasets 2.7.0 - Tokenizers 0.13.2

8bd1bfffa4c6da7498ffbec872d4fcc4

mayrajeo/maskrcnn-deadwood

mayrajeo

null

10

0

null

0

null

false

apache-2.0

null

0

['UAV', 'Deadwood', 'detectron2']

false

true

5,696

false

## Model description These models were trained for detecting both standing and fallen deadwood from UAV RGB images. All model configurations and weights here are fine-tuned from models available in [Detectron2 Model Zoo](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md). The models are trained on 512x512px RGB image patches with spatial resolution between 3.9 cm and 4.3 cm and with hand-annotated deadwood data based on visual inspection. The location of training dataset is in the vicinity of Hiidenportti national, Sotkamo, Finland, and the images were acquired during leaf-on season, at 16. and 17.7.2019. Most likely the models are most suitable to use with imagery from leaf-on season and with similar ground sampling distance. Example app is running on [https://huggingface.co/spaces/mayrajeo/maskrcnn-deadwood](https://huggingface.co/spaces/mayrajeo/maskrcnn-deadwood), which uses R101 backbone without TTA. ## Training data These models were trained on expert-annotated deadwood data, acquired on during leaf-on season at 16.-17.7.2019 from Hiidenportti, Sotkamo, Eastern-Finland. The ground resolution for the data varied between 3.9 and 4.4 cm. In addition, the model was tested with data collected from Evo, Hämeenlinna, Southern-Finland, acquired on 11.7.2018. The data from Evo was used only for testing the models. ## Results Patch-level data are non-overlapping 512x512 pixel non-overlapping tiles extracted from larger virtual plots. The results presented here are the run with test-time augmentation. Scene-level data are the full virtual plots extracted from the full images. For Hiidenportti, the virtual plot sizes vary between 2560x2560px and 8192x4864px. These patches contain also non-annotated buffer areas in order to extract the complete annotated area. For Sudenpesänkangas, all 71 scenes are 100x100 meters (2063x2062) pixels, and during inference they are extracted from the full mosaic with enough buffer to cover the full area. The results presented here are run for 512x512 pixel tiles with 256 px overlap, with both edge filtering and mask merging described in the workflow. ### Hiidenportti test set Hiidenportti test set contains 241 non-overlapping 512x512 pixel image patches, extracted from 5 scenes that cover 11 circular field plots. |Model|Patch AP50|Patch AP|Patch AP-groundwood|Patch AP-uprightwood|Scene AP50|Scene AP|Scene AP-groundwood|Scene AP-uprightwood| |----|-----------|--------|-------------------|--------------------|-----------|--------|-------------------|--------------------| |mask_rcnn_R_50_FPN_3x|0.654|0.339|0.316|0.363|0.640|0.315|0.235|0.396| |mask_rcnn_R_101_FPN_3x|**0.704**|**0.366**|0.326|**0.406**|**0.683**|**0.341**|0.246|**0.436**| |mask_rcnn_X_101_32x8d_FPN_3x|0.679|0.355|**0.333**|0.377|0.661|0.333|**0.255**|0.412| |cascade_mask_rcnn_R_50_FPN_3x|0.652|0.345|0.306|0.384|0.623|0.317|0.223|0.411| ### Evo dataset Sudenpesänkangas dataset contains 798 on-overlapping 512x512 pixel image patches, extracted from 71 scenes. |Model|Patch AP50|Patch AP|Patch AP-groundwood|Patch AP-uprightwood|Scene AP50|Scene AP|Scene AP-groundwood|Scene AP-uprightwood| |----|-----------|--------|-------------------|--------------------|-----------|--------|-------------------|--------------------| |mask_rcnn_R_50_FPN_3x|0.486|0.237|0.175|0.299|0.474|0.221|0.152|0.290| |mask_rcnn_R_101_FPN_3x|**0.519**|**0.252**|**0.183**|0.321|**0.511**|**0.236**|**0.160**|**0.311**| |mask_rcnn_X_101_32x8d_FPN_3x|0.502|0.245|0.182|0.307|0.494|0.232|0.159|0.305| |cascade_mask_rcnn_R_50_FPN_3x|0.497|0.248|0.172|**0.323**|0.473|0.225|0.148|0.302| ## How to use ### Running for small patches ```python from detectron2.engine import DefaultPredictor from detectron2.config import get_cfg from detectron2.data import build_detection_test_loader import cv2 cfg = get_cfg() cfg.merge_from_file(<path_to_model_config>) cfg.OUTPUT_DIR = '<path_to_output>' cfg.MODEL.WEIGHTS = '<path_to_weights>' cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # score threshold for detections predictor = DefaultPredictor(cfg) img = cv2.imread('<path_to_image_patch>') outputs = predictor(image) ``` ### Running for larger scenes Running on larger scenes requires the following steps: 1. Tiling the scenes into smaller image patches, optionally with overlap 2. Running the model on these smaller patches 3. Gathering the predictions into a single GIS data file 4. Optionally post-processing the results [drone_detector](https://jaeeolma.github.io/drone_detector) package has helpers for this: ```python from drone_detector.engines.detectron2.predict import predict_instance_masks predict_instance_masks( path_to_model_config='<path_to_model_config>', # model config file path_to_image='<path_to_image>', # which image to process outfile='<name_for_predictions>.geojson', # where to save the results processing_dir='temp', # directory for temporary files, deleted afterwards. Default: temp tile_size=512, # image patch size in pixels, square patches. Default: 400 tile_overlap=256, # overlap between tiles. Default: 100 smooth_preds=False, # not yet implemented, at some points runs dilation+erosion to smooth polygons. Default: False coco_set='<path_to_coco>', # the coco set the model was trained on to infer the class names. None defaults to dummy categories. Default: None postproc_results=True # whether to discard masks in the edge regions of patches Default: False ) ``` Also, after installing the package, `predict_instance_masks_detectron2` can be used as CLI command with identical syntax. When provided models default to dummy classes, **1** is *standing deadwood* and **2** is *fallen deadwood*.

1b4f7bf5adc240fa863e67c24089ed62

DOOGLAK/Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

6

transformers

0

token-classification

true

false

apache-2.0

null

['tagged_uni250v3_wikigold_split']

null

0

['generated_from_trainer']

true

1,565

false

# Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3093 - Precision: 0.5831 - Recall: 0.4849 - F1: 0.5295 - Accuracy: 0.8989 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 78 | 0.3468 | 0.3486 | 0.2362 | 0.2816 | 0.8670 | | No log | 2.0 | 156 | 0.3071 | 0.5484 | 0.4516 | 0.4953 | 0.8943 | | No log | 3.0 | 234 | 0.3093 | 0.5831 | 0.4849 | 0.5295 | 0.8989 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

88daa8f319f9f0ddb876cfa5bf0b93ca

StonyBrookNLP/t5-large-drop

StonyBrookNLP

t5

8

3

transformers

0

text2text-generation

true

false

cc-by-4.0

null

0

['question-answering, multi-step-reasoning, multi-hop-reasoning']

false

true

2,617

false

# What's this? This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496). This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details. We release the following models: - **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}` - **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}` - **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}` The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`. The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`. The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**. # How to use it? Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/t5-large-drop" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization model = AutoModelForSeq2SeqLM.from_pretrained(model_name) enable_digit_tokenization(tokenizer) input_texts = [ "answer_me: Who scored the first touchdown of the game?" + "context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..." # Note: some models have slightly different qn/ctxt format. See the github repo. ] input_ids = tokenizer( input_texts, return_tensors="pt", truncation=True, max_length=800, add_special_tokens=True, padding=True, )["input_ids"] generated_ids = model.generate(input_ids, min_length=1, max_length=50) generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False) generated_predictions = [ tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions ] # => ["Chaz Schilens"] ```

c1f5b98d3d6f0cc2a0d691c267d083af

silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data_3labels

silviacamplani

distilbert

18

2

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

2,794

false

# silviacamplani/distilbert-base-uncased-finetuned-dapt-ner-ai_data_3labels This model is a fine-tuned version of [silviacamplani/distilbert-base-uncased-finetuned-ai_data](https://huggingface.co/silviacamplani/distilbert-base-uncased-finetuned-ai_data) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6153 - Validation Loss: 0.5610 - Train Precision: 0.0 - Train Recall: 0.0 - Train F1: 0.0 - Train Accuracy: 0.8840 - Epoch: 9 ## 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: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 60, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 1.6831 | 1.5093 | 0.0031 | 0.0020 | 0.0024 | 0.8573 | 0 | | 1.3863 | 1.2247 | 0.0 | 0.0 | 0.0 | 0.8839 | 1 | | 1.1078 | 0.9646 | 0.0 | 0.0 | 0.0 | 0.8840 | 2 | | 0.8884 | 0.7566 | 0.0 | 0.0 | 0.0 | 0.8840 | 3 | | 0.7235 | 0.6484 | 0.0 | 0.0 | 0.0 | 0.8840 | 4 | | 0.6686 | 0.6034 | 0.0 | 0.0 | 0.0 | 0.8840 | 5 | | 0.6541 | 0.5804 | 0.0 | 0.0 | 0.0 | 0.8840 | 6 | | 0.6347 | 0.5687 | 0.0 | 0.0 | 0.0 | 0.8840 | 7 | | 0.6147 | 0.5630 | 0.0 | 0.0 | 0.0 | 0.8840 | 8 | | 0.6153 | 0.5610 | 0.0 | 0.0 | 0.0 | 0.8840 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

0ecaf6c3235bc4d51e5fb3d6e223a21a

jonatasgrosman/exp_w2v2t_en_vp-fr_s118

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

475

false

# exp_w2v2t_en_vp-fr_s118 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

41596bb1f08689eab92a082479710ad7

Jinchen/bert-base-cased-wikitext2-test-mlm

Jinchen

bert

7

1

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,189

false

# bert-base-cased-wikitext2-test-mlm This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.125 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - training precision: Mixed Precision ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.10.0+rocm4.2 - Datasets 2.3.2 - Tokenizers 0.12.1

010c5c994223d72d0bb8b22def0cacb8

speechbrain/asr-wav2vec2-ctc-aishell

speechbrain

null

7

15

speechbrain

4

null

true

false

apache-2.0

['zh']

['aishell']

null

1

0

1

0

['CTC', 'wav2vec2', 'pytorch', 'speechbrain']

false

true

3,980

false

97fed5c5b627162caea4f0b897adf42c

bookbot/wav2vec2-xls-r-adult-child-id-cls

bookbot

wav2vec2

10

3

transformers

0

audio-classification

true

false

apache-2.0

['id']

null

0

['audio-classification', 'generated_from_trainer']

true

2,561

false

# Wav2Vec2 XLS-R Adult/Child Indonesian Speech Classifier Wav2Vec2 XLS-R Adult/Child Indonesian Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a private adult/child Indonesian speech classification dataset. This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard. ## Model | Model | #params | Arch. | Training/Validation data (text) | | ----------------------------------- | ------- | ----- | ---------------------------------------------------- | | `wav2vec2-xls-r-adult-child-id-cls` | 300M | XLS-R | Adult/Child Indonesian Speech Classification Dataset | ## Evaluation Results The model achieves the following results on evaluation: | Dataset | Loss | Accuracy | F1 | | -------------------------------------------- | ------ | -------- | ------ | | Adult/Child Indonesian Speech Classification | 0.1970 | 93.38% | 0.9307 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - `learning_rate`: 3e-05 - `train_batch_size`: 8 - `eval_batch_size`: 8 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 32 - `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08` - `lr_scheduler_type`: linear - `lr_scheduler_warmup_ratio`: 0.1 - `num_epochs`: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | :-----------: | :---: | :--: | :-------------: | :------: | :----: | | 0.336 | 1.0 | 305 | 0.3146 | 0.8845 | 0.8698 | | 0.2345 | 2.0 | 610 | 0.2140 | 0.9251 | 0.9202 | | 0.3215 | 3.0 | 915 | 0.2038 | 0.9315 | 0.9286 | | 0.2059 | 4.0 | 1220 | 0.1970 | 0.9338 | 0.9307 | ## Disclaimer Do consider the biases which came from pre-training datasets that may be carried over into the results of this model. ## Authors Wav2Vec2 XLS-R Adult/Child Indonesian Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle. ## Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.2.0 - Tokenizers 0.12.1

3484ad7c2f7aee1a09cd0a72a1b94641

jcmc/wav2vec2-xls-r-1b-ir

jcmc

wav2vec2

34

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ga-IE']

['common_voice']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer']

true

1,670

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GA-IE dataset. It achieves the following results on the evaluation set: - Loss: 1.6569 - Wer: 0.8623 ## 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: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1851 | 15.62 | 500 | 1.8067 | 0.9256 | | 2.1586 | 31.25 | 1000 | 1.7883 | 0.9180 | | 2.0302 | 46.86 | 1500 | 1.7571 | 0.9192 | | 1.8706 | 62.49 | 2000 | 1.6314 | 0.8858 | | 1.7008 | 78.12 | 2500 | 1.6131 | 0.8679 | | 1.4982 | 93.74 | 3000 | 1.6540 | 0.8650 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

b63bfdc95cb547ce41efa94099f9402a

lgris/wav2vec2-large-xlsr-coraa-portuguese-cv8

lgris

wav2vec2

14

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['mozilla-foundation/common_voice_8_0']

null

0

['generated_from_trainer']

true

4,312

false

# wav2vec2-large-xlsr-coraa-portuguese-cv8 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1626 - Wer: 0.1365 ## 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5614 | 0.1 | 100 | 0.2542 | 0.1986 | | 0.5181 | 0.19 | 200 | 0.2740 | 0.2146 | | 0.5056 | 0.29 | 300 | 0.2472 | 0.2068 | | 0.4747 | 0.39 | 400 | 0.2464 | 0.2166 | | 0.4627 | 0.48 | 500 | 0.2277 | 0.2041 | | 0.4403 | 0.58 | 600 | 0.2245 | 0.1977 | | 0.4413 | 0.68 | 700 | 0.2156 | 0.1968 | | 0.437 | 0.77 | 800 | 0.2102 | 0.1919 | | 0.4305 | 0.87 | 900 | 0.2130 | 0.1864 | | 0.4324 | 0.97 | 1000 | 0.2144 | 0.1902 | | 0.4217 | 1.06 | 1100 | 0.2230 | 0.1891 | | 0.3823 | 1.16 | 1200 | 0.2033 | 0.1774 | | 0.3641 | 1.25 | 1300 | 0.2143 | 0.1830 | | 0.3707 | 1.35 | 1400 | 0.2034 | 0.1793 | | 0.3767 | 1.45 | 1500 | 0.2029 | 0.1823 | | 0.3483 | 1.54 | 1600 | 0.1999 | 0.1740 | | 0.3577 | 1.64 | 1700 | 0.1928 | 0.1728 | | 0.3667 | 1.74 | 1800 | 0.1898 | 0.1726 | | 0.3283 | 1.83 | 1900 | 0.1920 | 0.1688 | | 0.3571 | 1.93 | 2000 | 0.1904 | 0.1649 | | 0.3467 | 2.03 | 2100 | 0.1994 | 0.1648 | | 0.3145 | 2.12 | 2200 | 0.1940 | 0.1682 | | 0.3186 | 2.22 | 2300 | 0.1879 | 0.1571 | | 0.3058 | 2.32 | 2400 | 0.1975 | 0.1678 | | 0.3096 | 2.41 | 2500 | 0.1877 | 0.1589 | | 0.2964 | 2.51 | 2600 | 0.1862 | 0.1568 | | 0.3068 | 2.61 | 2700 | 0.1809 | 0.1588 | | 0.3036 | 2.7 | 2800 | 0.1769 | 0.1573 | | 0.3084 | 2.8 | 2900 | 0.1836 | 0.1524 | | 0.3109 | 2.9 | 3000 | 0.1807 | 0.1519 | | 0.2969 | 2.99 | 3100 | 0.1851 | 0.1516 | | 0.2698 | 3.09 | 3200 | 0.1737 | 0.1490 | | 0.2703 | 3.19 | 3300 | 0.1759 | 0.1457 | | 0.2759 | 3.28 | 3400 | 0.1778 | 0.1471 | | 0.2728 | 3.38 | 3500 | 0.1717 | 0.1462 | | 0.2398 | 3.47 | 3600 | 0.1767 | 0.1451 | | 0.256 | 3.57 | 3700 | 0.1742 | 0.1410 | | 0.2712 | 3.67 | 3800 | 0.1674 | 0.1414 | | 0.2648 | 3.76 | 3900 | 0.1717 | 0.1423 | | 0.2576 | 3.86 | 4000 | 0.1672 | 0.1403 | | 0.2504 | 3.96 | 4100 | 0.1683 | 0.1381 | | 0.2406 | 4.05 | 4200 | 0.1685 | 0.1399 | | 0.2403 | 4.15 | 4300 | 0.1656 | 0.1381 | | 0.2233 | 4.25 | 4400 | 0.1687 | 0.1371 | | 0.2546 | 4.34 | 4500 | 0.1642 | 0.1377 | | 0.2431 | 4.44 | 4600 | 0.1655 | 0.1372 | | 0.2337 | 4.54 | 4700 | 0.1625 | 0.1370 | | 0.2607 | 4.63 | 4800 | 0.1618 | 0.1363 | | 0.2292 | 4.73 | 4900 | 0.1622 | 0.1366 | | 0.2232 | 4.83 | 5000 | 0.1626 | 0.1365 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

643f79b5d866f23ffebb9841bd6364dc

naclbit/trinart_characters_19.2m_stable_diffusion_v1

naclbit

null

19

214

diffusers

142

text-to-image

false

creativeml-openrail-m

null

4

1

2

1

8

6

2

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image']

false

true

5,537

false

## Note A newer version of this model has been released: https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion ## Stable Diffusion TrinArt Characters model v1 trinart_characters_19.2m_stable_diffusion_v1 is a stable diffusion v1-based model trained by roughly 19.2M anime/manga style images (pre-rolled augmented images included) plus final finetuning by about 50,000 images. This model seeks for a sweet spot between artistic style versatility and anatomical quality within the given model spec of SDv1. This is the same version 1 model that was released in AI Novelist/TrinArt service from early September through Oct 14. We are currently experimenting with the new Derrida model on TrinArt service for further improvement and anatomical stabilization. In the mean time, please enjoy this real-service-tested Characters v1! #### Hardware - 8xNVIDIA A100 40GB #### Custom autoencoder *Note: There was a wrong checkpoint uploaded before 5 Nov 2022. The file has been replaced with the latest checkpoint.* We also provide a separate checkpoint for the custom KL autoencoder. As suggested by the Latent Diffusion paper, we found that training the autoencoder and the latent diffusion model separately improves the result. Since the official stable diffusion script does not support loading the other VAE, in order to run it in your script, you'll need to override state_dict for first_stage_model. The popular WebUI has the script to load separate first_stage_model parameters. #### Safety The dataset is filtered to exclude NSFW or unsafe contents. After our extensive experimentation and testing with 10M+ user generated images, we decided that this model is safe enough and less likely to spit out questionable (nudity/overly sexual/realistic gore) content than the stock SD v1.4 model or other anime/manga models. However, if the user tortures this model enough until it talks, it may be still possible to force this model to generate obnoxious materials. We do not consider this model to be 100% risk-free. *This statement does not necessarily restrict third-party from training a derivative of this model that includes NSFW. #### Examples Below images are directly generated by the native TrinArt service with its idiosyncratic upscaler, parser and processes. Your mileage may vary. ![examples](https://pbs.twimg.com/media/FeLNpeuVIAEUqKe?format=jpg&name=4096x4096) (assorted random examples) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221005_c.webp) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221005_d.webp) wide shot, high quality, htgngg animal arm rest brown hair merry chair cup dress flower from above jacket on shoulders long hair sitting solo sugar bowl fantasy adventurer's inn table teacup teapot landscape miniature (2022 Artstyle preset) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221016_a.webp) highres wide shot bangs bare shoulders water bird cage terrarium detached sleeves frilled frilled legwear frills hair ornament hair ribbon hood long hair medium breasts ribbon thighhighs (2019 Artstyle preset) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221006_a.webp) 1girl standing holding sword hizzrd arm up bangs bare shoulders boots bow breasts bright pupils choker detached sleeves diamond (shape) floating floating hair footwear bow from side full body gloves leg up long hair looking at viewer open mouth outstretched arm solo streaked hair swept bangs two tone hair very long hair::4 angry::1 (2022 Artstyle preset) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221006_c.webp) 1boy male focus standing hizzrd holding sword arm up bow bright pupils cape coat diamond (shape) floating floating hair fold-over boots footwear bow from side full body gloves leg up long sleeves looking at viewer open mouth outstretched arm open coat open clothes solo swept two tone hair thigh boots::4 angry::1.25 (2022 Artstyle preset) ![examples](https://pbs.twimg.com/media/FeNv2QeUAAIvDO1?format=jpg&name=large) cathedral 1girl schoolgirl momoko school uniform cats particles beautiful shooting stars detailed cathedral jacket open mouth glasses cats (2022 Artstyle preset) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221005_a.webp) highres 2girls yuri wide shot bangs bare shoulders water bird cage terrarium detached sleeves frilled frilled legwear frills hair ornament hair ribbon hood long hair medium breasts ribbon thighhighs (More Details preset) ![examples](https://ai-novel.com/images/trinart_samples/trinart_sample_20221016_d.webp) wide shot, best quality lapis erebcir highres 1boy bangs black gloves brown hair closed mouth gloves hair between eyes looking at viewer male focus flowers green eyes (More Details preset) TrinArt 2022 Artstyle preset negative prompts: **retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019** TrinArt More Details preset negative prompts: **flat color, flat shading** We recommend to add known sets of negative prompts in order to stabilize the anatomy such as: bad hands, fewer digits, etc. #### Credits - Sta, AI Novelist Dev (https://ai-novel.com/) @ Bit192, Inc. Twitter https://twitter.com/naclbbr (Japanese) https://twitter.com/naclbbre (English) - Stable Diffusion - Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn #### License CreativeML OpenRAIL-M

4d2ce52f4b5fe5a036c86ba8405507a7

SBB/sbb_ner

SBB

null

5

27

transformers

2

token-classification

true

false

apache-2.0

['de']

['conll2003', 'germeval_14']

null

1

0

['pytorch', 'token-classification', 'sequence-tagger-model']

false

true

1,560

false

# About `sbb_ner` This is a BERT model for named entity recognition (NER) in historical German. It predicts the classes `PER`, `LOC` and `ORG`. The model is based on the 🤗 [`BERT base multilingual cased`](https://huggingface.co/bert-base-multilingual-cased) model. We applied unsupervised pre-training on 2,333,647 pages of unlabeled historical German text from the Berlin State Library digital collections, and supervised pre-training on two datasets with contemporary German text, [conll2003](https://huggingface.co/models?dataset=dataset:conll2003) and [germeval_14](https://huggingface.co/models?dataset=dataset:germeval_14). For further details, have a look at [sbb_ner](https://github.com/qurator-spk/sbb_ner) on GitHub. # Results In a 5-fold cross validation with different historical German NER corpora (see our *KONVENS2019* [paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf)), the model obtained an F1-Score of **84.3**±1.1%. In the *CLEF-HIPE-2020* Shared Task ([paper](http://ceur-ws.org/Vol-2696/paper_255.pdf)), the model ranked 2nd of 13 systems for the German coarse NER task. # Weights We provide model weights for PyTorch. | Model | Downloads | ------------------------| ------------------------ | `bert-sbb-de-finetuned` | [`config.json`](https://huggingface.co/SBB/sbb_ner/blob/main/config.json) • [`pytorch_model_ep7.bin`](https://huggingface.co/SBB/sbb_ner/blob/main/pytorch_model_ep7.bin) • [`vocab.txt`](https://huggingface.co/SBB/sbb_ner/blob/main/vocab.txt)

d4e33a6ac76965619ad7bf083f2f07ce

tiagoblima/punctuation-finetune-mec

tiagoblima

bert

10

12

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,232

false

# punctuation-finetune-mec This model is a fine-tuned version of [tiagoblima/punctuation-taboa-bert](https://huggingface.co/tiagoblima/punctuation-taboa-bert) on an unknown dataset. ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 411 | 0.1356 | 0.9791 | 0.7083 | 0.8220 | 0.9553 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

a70b08daae9006af833bc10d45f6be42

ryusangwon/xlm-roberta-base-fintuned-panx-de

ryusangwon

xlm-roberta

19

1

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,317

false

# xlm-roberta-base-fintuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1389 - F1: 0.8638 ## 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: 24 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 525 | 0.1658 | 0.8206 | | No log | 2.0 | 1050 | 0.1413 | 0.8486 | | No log | 3.0 | 1575 | 0.1389 | 0.8638 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

3c21608274adb766d8fff590dbeec194