readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - i2b22014
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-deid2014-ner-v5-classweights
    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: 0.8832236842105263
      - name: Recall
        type: recall
        value: 0.6910561632502987
      - name: F1
        type: f1
        value: 0.7754112732711052
      - name: Accuracy
        type: accuracy
        value: 0.9883040491052534
  ---
  
  <!-- 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-v5-classweights
  
  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: 0.0009
  - Precision: 0.8832
  - Recall: 0.6911
  - F1: 0.7754
  - Accuracy: 0.9883
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0001        | 1.0   | 1838  | 0.0008          | 0.7702    | 0.3780 | 0.5071 | 0.9771   |
  | 0.0           | 2.0   | 3676  | 0.0007          | 0.8753    | 0.5671 | 0.6883 | 0.9827   |
  | 0.0           | 3.0   | 5514  | 0.0006          | 0.8074    | 0.4128 | 0.5463 | 0.9775   |
  | 0.0           | 4.0   | 7352  | 0.0007          | 0.8693    | 0.6102 | 0.7170 | 0.9848   |
  | 0.0           | 5.0   | 9190  | 0.0006          | 0.8710    | 0.6022 | 0.7121 | 0.9849   |
  | 0.0           | 6.0   | 11028 | 0.0007          | 0.8835    | 0.6547 | 0.7521 | 0.9867   |
  | 0.0           | 7.0   | 12866 | 0.0009          | 0.8793    | 0.6661 | 0.7579 | 0.9873   |
  | 0.0           | 8.0   | 14704 | 0.0008          | 0.8815    | 0.6740 | 0.7639 | 0.9876   |
  | 0.0           | 9.0   | 16542 | 0.0009          | 0.8812    | 0.6851 | 0.7709 | 0.9880   |
  | 0.0           | 10.0  | 18380 | 0.0009          | 0.8832    | 0.6911 | 0.7754 | 0.9883   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  