SpanMarker with numind/generic-entity_recognition_NER-multilingual-v1 on wikiann
This is a SpanMarker model trained on the wikiann dataset that can be used for Named Entity Recognition. This SpanMarker model uses numind/generic-entity_recognition_NER-multilingual-v1 as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: numind/generic-entity_recognition_NER-multilingual-v1
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 9 words
- Training Dataset: wikiann
- Language: de
- License: mit
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
Label | Examples |
---|---|
LOC | "Savoyer Voralpen", "Bagan", "Zechin" |
ORG | "NHL Entry Draft", "SKA Sankt Petersburg", "Minnesota Wild" |
PER | "Antonina Wladimirowna Kriwoschapka", "Lou Salomé", "Jaan Kirsipuu" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.9070 | 0.9070 | 0.9070 |
LOC | 0.9036 | 0.9298 | 0.9165 |
ORG | 0.8638 | 0.8446 | 0.8541 |
PER | 0.9507 | 0.9405 | 0.9455 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("Sein Bundesliga-Debüt gab der Angreifer am 23.")
Downstream Use
You can finetune this model on your own dataset.
Click to expand
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Sentence length | 1 | 9.7693 | 85 |
Entities per sentence | 1 | 1.3821 | 20 |
Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
1.2658 | 200 | 0.0172 | 0.8842 | 0.8534 | 0.8686 | 0.9586 |
2.5316 | 400 | 0.0145 | 0.8977 | 0.8889 | 0.8933 | 0.9670 |
3.7975 | 600 | 0.0161 | 0.8962 | 0.9006 | 0.8984 | 0.9688 |
5.0633 | 800 | 0.0180 | 0.8982 | 0.8996 | 0.8989 | 0.9689 |
6.3291 | 1000 | 0.0201 | 0.9014 | 0.9008 | 0.9011 | 0.9694 |
7.5949 | 1200 | 0.0201 | 0.9010 | 0.9057 | 0.9033 | 0.9702 |
8.8608 | 1400 | 0.0217 | 0.9062 | 0.9036 | 0.9049 | 0.9702 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0
Citation
BibTeX
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
- Downloads last month
- 12
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for davanstrien/numind_generic-entity_recognition_NER-multilingual-v1_wikiann_de
Base model
numind/NuNER-multilingual-v0.1Dataset used to train davanstrien/numind_generic-entity_recognition_NER-multilingual-v1_wikiann_de
Evaluation results
- F1 on Unknownself-reported0.907
- Precision on Unknownself-reported0.907
- Recall on Unknownself-reported0.907