readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ncbi_disease
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-NCBI-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ncbi_disease
        type: ncbi_disease
        config: ncbi_disease
        split: train
        args: ncbi_disease
      metrics:
      - name: Precision
        type: precision
        value: 0.8083491461100569
      - name: Recall
        type: recall
        value: 0.8875
      - name: F1
        type: f1
        value: 0.846077457795432
      - name: Accuracy
        type: accuracy
        value: 0.9820794382985671
  ---
  
  <!-- 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. -->
  
  # electramed-small-NCBI-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ncbi_disease dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0664
  - Precision: 0.8083
  - Recall: 0.8875
  - F1: 0.8461
  - Accuracy: 0.9821
  
  ## Model description
  
  More information needed
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.4787        | 1.0   | 340  | 0.5090          | 0.6090    | 0.5062 | 0.5529 | 0.9608   |
  | 0.2029        | 2.0   | 680  | 0.1890          | 0.7643    | 0.8208 | 0.7916 | 0.9774   |
  | 0.1402        | 3.0   | 1020 | 0.1106          | 0.7839    | 0.8802 | 0.8292 | 0.9807   |
  | 0.075         | 4.0   | 1360 | 0.0876          | 0.8162    | 0.8698 | 0.8422 | 0.9817   |
  | 0.0408        | 5.0   | 1700 | 0.0776          | 0.8090    | 0.8781 | 0.8422 | 0.9818   |
  | 0.0308        | 6.0   | 2040 | 0.0697          | 0.8044    | 0.8823 | 0.8415 | 0.9825   |
  | 0.0405        | 7.0   | 2380 | 0.0680          | 0.8118    | 0.8854 | 0.8470 | 0.9830   |
  | 0.0138        | 8.0   | 2720 | 0.0665          | 0.8111    | 0.8854 | 0.8466 | 0.9826   |
  | 0.0223        | 9.0   | 3060 | 0.0675          | 0.8064    | 0.8896 | 0.8460 | 0.9821   |
  | 0.0395        | 10.0  | 3400 | 0.0664          | 0.8083    | 0.8875 | 0.8461 | 0.9821   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  