readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - i2b22014
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-deid2014-ner-v3
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: i2b22014
        type: i2b22014
        config: i2b22014-deid
        split: train
        args: i2b22014-deid
      metrics:
      - name: Precision
        type: precision
        value: 0.7776378519384726
      - name: Recall
        type: recall
        value: 0.7946502435885652
      - name: F1
        type: f1
        value: 0.7860520094562647
      - name: Accuracy
        type: accuracy
        value: 0.9908687950002661
  ---
  
  <!-- 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-deid2014-ner-v3
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0354
  - Precision: 0.7776
  - Recall: 0.7947
  - F1: 0.7861
  - Accuracy: 0.9909
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0125        | 1.0   | 1838  | 0.1338          | 0.3514    | 0.3812 | 0.3657 | 0.9715   |
  | 0.0032        | 2.0   | 3676  | 0.0856          | 0.4444    | 0.5156 | 0.4774 | 0.9778   |
  | 0.0012        | 3.0   | 5514  | 0.0678          | 0.5222    | 0.5994 | 0.5581 | 0.9819   |
  | 0.0006        | 4.0   | 7352  | 0.0547          | 0.6900    | 0.7025 | 0.6962 | 0.9865   |
  | 0.018         | 5.0   | 9190  | 0.0466          | 0.7227    | 0.7468 | 0.7345 | 0.9881   |
  | 0.0002        | 6.0   | 11028 | 0.0419          | 0.7396    | 0.7664 | 0.7528 | 0.9891   |
  | 0.0002        | 7.0   | 12866 | 0.0390          | 0.7730    | 0.7693 | 0.7712 | 0.9901   |
  | 0.0002        | 8.0   | 14704 | 0.0368          | 0.7778    | 0.7822 | 0.7800 | 0.9906   |
  | 0.0001        | 9.0   | 16542 | 0.0359          | 0.7765    | 0.7898 | 0.7831 | 0.9907   |
  | 0.0001        | 10.0  | 18380 | 0.0354          | 0.7776    | 0.7947 | 0.7861 | 0.9909   |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  