readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_EN
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_EN
  
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the CRAFT dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2299
  - Precision: 0.8122
  - Recall: 0.8475
  - F1: 0.8294
  - Accuracy: 0.9661
  
  ## 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.
  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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0542        | 1.0   | 2719  | 0.1540          | 0.7834    | 0.8300 | 0.8060 | 0.9622   |
  | 0.0229        | 2.0   | 5438  | 0.1920          | 0.8092    | 0.8219 | 0.8155 | 0.9644   |
  | 0.0069        | 3.0   | 8157  | 0.2054          | 0.8130    | 0.8481 | 0.8302 | 0.9656   |
  | 0.0023        | 4.0   | 10876 | 0.2299          | 0.8122    | 0.8475 | 0.8294 | 0.9661   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  