readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - bc5_cdr
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-BC5CDR-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc5_cdr
        type: bc5_cdr
        config: BC5CDR-Disease
        split: train
        args: BC5CDR-Disease
      metrics:
      - name: Precision
        type: precision
        value: 0.8091945430760605
      - name: Recall
        type: recall
        value: 0.882862677133245
      - name: F1
        type: f1
        value: 0.8444249427136657
      - name: Accuracy
        type: accuracy
        value: 0.9685851703406814
  ---
  
  <!-- 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-BC5CDR-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc5_cdr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1227
  - Precision: 0.8092
  - Recall: 0.8829
  - F1: 0.8444
  - Accuracy: 0.9686
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7177        | 1.0   | 286  | 0.6902          | 0.0       | 0.0    | 0.0    | 0.8864   |
  | 0.1561        | 2.0   | 572  | 0.3210          | 0.7334    | 0.8104 | 0.7700 | 0.9636   |
  | 0.2511        | 3.0   | 858  | 0.2064          | 0.7809    | 0.8711 | 0.8236 | 0.9666   |
  | 0.0512        | 4.0   | 1144 | 0.1599          | 0.7937    | 0.8751 | 0.8324 | 0.9689   |
  | 0.083         | 5.0   | 1430 | 0.1449          | 0.7983    | 0.8804 | 0.8373 | 0.9679   |
  | 0.0412        | 6.0   | 1716 | 0.1315          | 0.8141    | 0.8825 | 0.8469 | 0.9701   |
  | 0.1437        | 7.0   | 2002 | 0.1258          | 0.8227    | 0.8758 | 0.8485 | 0.9699   |
  | 0.1894        | 8.0   | 2288 | 0.1226          | 0.8141    | 0.8833 | 0.8473 | 0.9696   |
  | 0.0236        | 9.0   | 2574 | 0.1220          | 0.8160    | 0.8824 | 0.8479 | 0.9694   |
  | 0.0602        | 10.0  | 2860 | 0.1227          | 0.8092    | 0.8829 | 0.8444 | 0.9686   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  