readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - bc4chemd
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-BC4CHEMD-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc4chemd
        type: bc4chemd
        config: bc4chemd
        split: train
        args: bc4chemd
      metrics:
      - name: Precision
        type: precision
        value: 0.7715624436835465
      - name: Recall
        type: recall
        value: 0.6760888102832959
      - name: F1
        type: f1
        value: 0.7206773498518718
      - name: Accuracy
        type: accuracy
        value: 0.9770623458780496
  ---
  
  <!-- 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-BC4CHEMD-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc4chemd dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0655
  - Precision: 0.7716
  - Recall: 0.6761
  - F1: 0.7207
  - Accuracy: 0.9771
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0882        | 1.0   | 1918  | 0.1058          | 0.6615    | 0.3942 | 0.4940 | 0.9635   |
  | 0.0555        | 2.0   | 3836  | 0.0820          | 0.7085    | 0.5133 | 0.5954 | 0.9689   |
  | 0.0631        | 3.0   | 5754  | 0.0769          | 0.6892    | 0.5743 | 0.6266 | 0.9699   |
  | 0.0907        | 4.0   | 7672  | 0.0682          | 0.7623    | 0.5923 | 0.6666 | 0.9740   |
  | 0.0313        | 5.0   | 9590  | 0.0675          | 0.7643    | 0.6223 | 0.6860 | 0.9749   |
  | 0.0306        | 6.0   | 11508 | 0.0662          | 0.7654    | 0.6398 | 0.6970 | 0.9754   |
  | 0.0292        | 7.0   | 13426 | 0.0656          | 0.7694    | 0.6552 | 0.7077 | 0.9763   |
  | 0.1025        | 8.0   | 15344 | 0.0658          | 0.7742    | 0.6687 | 0.7176 | 0.9769   |
  | 0.0394        | 9.0   | 17262 | 0.0662          | 0.7741    | 0.6731 | 0.7201 | 0.9770   |
  | 0.0378        | 10.0  | 19180 | 0.0655          | 0.7716    | 0.6761 | 0.7207 | 0.9771   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  