readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - i2b22014
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-deid2014-ner-v4
    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: 0.7571112095702259
      - name: Recall
        type: recall
        value: 0.7853663020498207
      - name: F1
        type: f1
        value: 0.770979967514889
      - name: Accuracy
        type: accuracy
        value: 0.9906153616114308
  ---
  
  <!-- 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-v4
  
  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: 0.0362
  - Precision: 0.7571
  - Recall: 0.7854
  - F1: 0.7710
  - Accuracy: 0.9906
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0143        | 1.0   | 1838  | 0.1451          | 0.3136    | 0.3463 | 0.3291 | 0.9700   |
  | 0.0033        | 2.0   | 3676  | 0.0940          | 0.4293    | 0.4861 | 0.4559 | 0.9758   |
  | 0.0014        | 3.0   | 5514  | 0.0725          | 0.4906    | 0.5766 | 0.5301 | 0.9799   |
  | 0.0007        | 4.0   | 7352  | 0.0568          | 0.6824    | 0.7022 | 0.6921 | 0.9860   |
  | 0.0112        | 5.0   | 9190  | 0.0497          | 0.6966    | 0.7400 | 0.7177 | 0.9870   |
  | 0.0002        | 6.0   | 11028 | 0.0442          | 0.7126    | 0.7549 | 0.7332 | 0.9878   |
  | 0.0002        | 7.0   | 12866 | 0.0404          | 0.7581    | 0.7591 | 0.7586 | 0.9896   |
  | 0.0002        | 8.0   | 14704 | 0.0376          | 0.7540    | 0.7804 | 0.7670 | 0.9904   |
  | 0.0002        | 9.0   | 16542 | 0.0367          | 0.7548    | 0.7825 | 0.7684 | 0.9905   |
  | 0.0001        | 10.0  | 18380 | 0.0362          | 0.7571    | 0.7854 | 0.7710 | 0.9906   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  