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
    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
  
  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: 0.1720
  - 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](https://github.com/UCDenver-ccp/CRAFT/releases)(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
  