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roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the CRAFT dataset. It achieves the following results on the evaluation set:
- Loss: 0.1720
- Precision: 0.8253
- Recall: 0.8147
- F1: 0.8200
- Accuracy: 0.9660
Model description
This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical.
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: 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1133 | 1.0 | 1360 | 0.1629 | 0.7985 | 0.7782 | 0.7882 | 0.9610 |
| 0.049 | 2.0 | 2720 | 0.1530 | 0.8165 | 0.8084 | 0.8124 | 0.9651 |
| 0.0306 | 3.0 | 4080 | 0.1603 | 0.8198 | 0.8075 | 0.8136 | 0.9650 |
| 0.0158 | 4.0 | 5440 | 0.1720 | 0.8253 | 0.8147 | 0.8200 | 0.9660 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
