<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN
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: 0.2276
- Precision: 0.8078
- Recall: 0.8258
- F1: 0.8167
- Accuracy: 0.9629
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. This model is trained on augmented data created using Entity Replacement. 20% of the entities were replaced using a list of entities for each entity tag obtained from the official ontologies for each entity class. Both datasets (original, augmented) were concatenated.
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 |
|---|---|---|---|---|---|---|---|
| 0.0842 | 1.0 | 2719 | 0.1765 | 0.7606 | 0.7785 | 0.7695 | 0.9542 |
| 0.0392 | 2.0 | 5438 | 0.1971 | 0.7990 | 0.7958 | 0.7974 | 0.9596 |
| 0.0138 | 3.0 | 8157 | 0.2094 | 0.8013 | 0.8196 | 0.8103 | 0.9620 |
| 0.0082 | 4.0 | 10876 | 0.2276 | 0.8078 | 0.8258 | 0.8167 | 0.9629 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
