readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es
    results: []
  ---
  
  <!-- 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_en_es
  
  This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/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](https://github.com/UCDenver-ccp/CRAFT/releases)(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
  