SpanMarker with microsoft/xtremedistil-l12-h384-uncased on FewNERD, CoNLL2003, and OntoNotes v5
This is a SpanMarker model trained on the FewNERD, CoNLL2003, and OntoNotes v5 dataset that can be used for Named Entity Recognition. This SpanMarker model uses microsoft/xtremedistil-l12-h384-uncased as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: microsoft/xtremedistil-l12-h384-uncased
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Training Dataset: FewNERD, CoNLL2003, and OntoNotes v5
- Language: en
- License: cc-by-sa-4.0
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
Label | Examples |
---|---|
ORG | "Texas Chicken", "IAEA", "Church 's Chicken" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.7620 | 0.7498 | 0.7559 |
ORG | 0.7620 | 0.7498 | 0.7559 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the π€ Hub
model = SpanMarkerModel.from_pretrained("nbroad/span-marker-xdistil-l12-h384-orgs-v3")
# Run inference
entities = model.predict("SCL claims that its methodology has been approved or endorsed by agencies of the Government of the United Kingdom and the Federal government of the United States, among others.")
Downstream Use
You can finetune this model on your own dataset.
Click to expand
from span_marker import SpanMarkerModel, Trainer
# Download from the π€ Hub
model = SpanMarkerModel.from_pretrained("nbroad/span-marker-xdistil-l12-h384-orgs-v3")
# 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("nbroad/span-marker-xdistil-l12-h384-orgs-v3-finetuned")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Sentence length | 1 | 23.5706 | 263 |
Entities per sentence | 0 | 0.7865 | 39 |
Training Hyperparameters
- learning_rate: 0.0003
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training Results
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
0.5720 | 600 | 0.0086 | 0.7150 | 0.7095 | 0.7122 | 0.9660 |
1.1439 | 1200 | 0.0074 | 0.7556 | 0.7253 | 0.7401 | 0.9682 |
1.7159 | 1800 | 0.0073 | 0.7482 | 0.7619 | 0.7550 | 0.9702 |
2.2879 | 2400 | 0.0072 | 0.7761 | 0.7573 | 0.7666 | 0.9713 |
2.8599 | 3000 | 0.0070 | 0.7691 | 0.7688 | 0.7689 | 0.9720 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0a0+32f93b1
- 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}
}
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Model tree for nbroad/span-marker-xdistil-l12-h384-orgs-v3
Base model
microsoft/xtremedistil-l12-h384-uncasedDataset used to train nbroad/span-marker-xdistil-l12-h384-orgs-v3
Evaluation results
- F1 on FewNERD, CoNLL2003, and OntoNotes v5test set self-reported0.756
- Precision on FewNERD, CoNLL2003, and OntoNotes v5test set self-reported0.762
- Recall on FewNERD, CoNLL2003, and OntoNotes v5test set self-reported0.750