readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ade_drug_dosage_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-ADE-DRUG-DOSAGE-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ade_drug_dosage_ner
        type: ade_drug_dosage_ner
        config: ade
        split: train
        args: ade
      metrics:
      - name: Precision
        type: precision
        value: 0.0
      - name: Recall
        type: recall
        value: 0.0
      - name: F1
        type: f1
        value: 0.0
      - name: Accuracy
        type: accuracy
        value: 0.8697318007662835
  ---
  
  <!-- 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-DOSAGE-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_dosage_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6064
  - Precision: 0.0
  - Recall: 0.0
  - F1: 0.0
  - Accuracy: 0.8697
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.4165        | 1.0   | 14   | 1.3965          | 0.0255    | 0.0636 | 0.0365 | 0.7471   |
  | 1.2063        | 2.0   | 28   | 1.1702          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.9527        | 3.0   | 42   | 0.9342          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.8238        | 4.0   | 56   | 0.7775          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.7452        | 5.0   | 70   | 0.6945          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.6386        | 6.0   | 84   | 0.6519          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.6742        | 7.0   | 98   | 0.6294          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.6669        | 8.0   | 112  | 0.6162          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.6595        | 9.0   | 126  | 0.6090          | 0.0       | 0.0    | 0.0    | 0.8697   |
  | 0.6122        | 10.0  | 140  | 0.6064          | 0.0       | 0.0    | 0.0    | 0.8697   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  