readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - bc2gm_corpus
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-BC2GM-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc2gm_corpus
        type: bc2gm_corpus
        config: bc2gm_corpus
        split: train
        args: bc2gm_corpus
      metrics:
      - name: Precision
        type: precision
        value: 0.7652071701439906
      - name: Recall
        type: recall
        value: 0.823399209486166
      - name: F1
        type: f1
        value: 0.7932373771989948
      - name: Accuracy
        type: accuracy
        value: 0.9756735092182762
  ---
  
  <!-- 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-BC2GM-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc2gm_corpus dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0720
  - Precision: 0.7652
  - Recall: 0.8234
  - F1: 0.7932
  - Accuracy: 0.9757
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.085         | 1.0   | 782  | 0.1112          | 0.6147    | 0.7777 | 0.6867 | 0.9634   |
  | 0.0901        | 2.0   | 1564 | 0.0825          | 0.7141    | 0.8028 | 0.7559 | 0.9720   |
  | 0.0303        | 3.0   | 2346 | 0.0759          | 0.7310    | 0.8049 | 0.7662 | 0.9724   |
  | 0.0037        | 4.0   | 3128 | 0.0735          | 0.7430    | 0.8168 | 0.7781 | 0.9735   |
  | 0.0325        | 5.0   | 3910 | 0.0723          | 0.7571    | 0.8142 | 0.7846 | 0.9748   |
  | 0.0582        | 6.0   | 4692 | 0.0701          | 0.7664    | 0.8144 | 0.7897 | 0.9759   |
  | 0.0073        | 7.0   | 5474 | 0.0701          | 0.7711    | 0.8212 | 0.7953 | 0.9761   |
  | 0.1031        | 8.0   | 6256 | 0.0712          | 0.7602    | 0.8258 | 0.7916 | 0.9756   |
  | 0.0248        | 9.0   | 7038 | 0.0722          | 0.7691    | 0.8231 | 0.7952 | 0.9759   |
  | 0.0136        | 10.0  | 7820 | 0.0720          | 0.7652    | 0.8234 | 0.7932 | 0.9757   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  