readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ade_drug_effect_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-ADE-DRUG-EFFECT-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ade_drug_effect_ner
        type: ade_drug_effect_ner
        config: ade
        split: train
        args: ade
      metrics:
      - name: Precision
        type: precision
        value: 0.7745054945054946
      - name: Recall
        type: recall
        value: 0.6555059523809523
      - name: F1
        type: f1
        value: 0.7100544025790851
      - name: Accuracy
        type: accuracy
        value: 0.9310355073540336
  ---
  
  <!-- 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-ADE-DRUG-EFFECT-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_effect_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1630
  - Precision: 0.7745
  - Recall: 0.6555
  - F1: 0.7101
  - Accuracy: 0.9310
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.4498        | 1.0   | 336  | 0.3042          | 0.5423    | 0.6295 | 0.5826 | 0.9114   |
  | 0.2572        | 2.0   | 672  | 0.2146          | 0.7596    | 0.6194 | 0.6824 | 0.9276   |
  | 0.1542        | 3.0   | 1008 | 0.1894          | 0.7806    | 0.6168 | 0.6891 | 0.9299   |
  | 0.1525        | 4.0   | 1344 | 0.1771          | 0.7832    | 0.625  | 0.6952 | 0.9309   |
  | 0.1871        | 5.0   | 1680 | 0.1723          | 0.7271    | 0.6920 | 0.7091 | 0.9304   |
  | 0.1425        | 6.0   | 2016 | 0.1683          | 0.7300    | 0.6979 | 0.7136 | 0.9297   |
  | 0.1638        | 7.0   | 2352 | 0.1654          | 0.7432    | 0.6771 | 0.7086 | 0.9306   |
  | 0.1592        | 8.0   | 2688 | 0.1635          | 0.7613    | 0.6585 | 0.7062 | 0.9305   |
  | 0.1882        | 9.0   | 3024 | 0.1625          | 0.7858    | 0.6373 | 0.7038 | 0.9309   |
  | 0.1339        | 10.0  | 3360 | 0.1630          | 0.7745    | 0.6555 | 0.7101 | 0.9310   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  