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Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es
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: 0.1750
- Precision: 0.8664
- Recall: 0.8587
- F1: 0.8625
- Accuracy: 0.9727
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 Spanish and 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 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 |
---|---|---|---|---|---|---|---|
0.0564 | 1.0 | 1360 | 0.1459 | 0.8296 | 0.8489 | 0.8392 | 0.9696 |
0.0222 | 2.0 | 2720 | 0.1554 | 0.8650 | 0.8320 | 0.8482 | 0.9702 |
0.0124 | 3.0 | 4080 | 0.1670 | 0.8588 | 0.8564 | 0.8576 | 0.9717 |
0.0052 | 4.0 | 5440 | 0.1750 | 0.8664 | 0.8587 | 0.8625 | 0.9727 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6