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_Augmented_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_Augmented_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: 0.2224
  - Precision: 0.8298
  - Recall: 0.8306
  - F1: 0.8302
  - Accuracy: 0.9659
  
  ## 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. Three datasets (original, augmented, MT translated CRAFT) 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0624        | 1.0   | 4078  | 0.1844          | 0.8002    | 0.7923 | 0.7963 | 0.9607   |
  | 0.0284        | 2.0   | 8156  | 0.1937          | 0.8394    | 0.7988 | 0.8186 | 0.9637   |
  | 0.0118        | 3.0   | 12234 | 0.2007          | 0.8285    | 0.8232 | 0.8258 | 0.9649   |
  | 0.0043        | 4.0   | 16312 | 0.2224          | 0.8298    | 0.8306 | 0.8302 | 0.9659   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  