readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ade_drug_effect_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-ADE-DRUG-EFFECT-ner-v3
    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: 0.7436108821104699
      - name: Recall
        type: recall
        value: 0.6711309523809523
      - name: F1
        type: f1
        value: 0.7055142745404771
      - name: Accuracy
        type: accuracy
        value: 0.9334986406954859
  ---
  
  <!-- 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-v3
  
  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: 0.1626
  - Precision: 0.7436
  - Recall: 0.6711
  - F1: 0.7055
  - Accuracy: 0.9335
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3393        | 1.0   | 336  | 0.3055          | 0.6126    | 0.6648 | 0.6376 | 0.9218   |
  | 0.2503        | 2.0   | 672  | 0.2138          | 0.7025    | 0.6905 | 0.6964 | 0.9300   |
  | 0.1494        | 3.0   | 1008 | 0.1879          | 0.7342    | 0.6555 | 0.6926 | 0.9326   |
  | 0.1152        | 4.0   | 1344 | 0.1755          | 0.7323    | 0.6797 | 0.7050 | 0.9327   |
  | 0.178         | 5.0   | 1680 | 0.1726          | 0.7279    | 0.6827 | 0.7045 | 0.9326   |
  | 0.1814        | 6.0   | 2016 | 0.1654          | 0.7358    | 0.6734 | 0.7032 | 0.9332   |
  | 0.1292        | 7.0   | 2352 | 0.1641          | 0.7332    | 0.6849 | 0.7082 | 0.9336   |
  | 0.1107        | 8.0   | 2688 | 0.1638          | 0.7520    | 0.6522 | 0.6985 | 0.9337   |
  | 0.1911        | 9.0   | 3024 | 0.1625          | 0.7503    | 0.6596 | 0.7020 | 0.9331   |
  | 0.1517        | 10.0  | 3360 | 0.1626          | 0.7436    | 0.6711 | 0.7055 | 0.9335   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  