readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - jnlpba
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-JNLPBA-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: jnlpba
        type: jnlpba
        config: jnlpba
        split: train
        args: jnlpba
      metrics:
      - name: Precision
        type: precision
        value: 0.8224512128396863
      - name: Recall
        type: recall
        value: 0.878188899707887
      - name: F1
        type: f1
        value: 0.8494066679223958
      - name: Accuracy
        type: accuracy
        value: 0.9620705451213926
  ---
  
  <!-- 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-JNLPBA-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1167
  - Precision: 0.8225
  - Recall: 0.8782
  - F1: 0.8494
  - Accuracy: 0.9621
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.398         | 1.0   | 2087  | 0.1941          | 0.7289    | 0.7936 | 0.7599 | 0.9441   |
  | 0.0771        | 2.0   | 4174  | 0.1542          | 0.7734    | 0.8348 | 0.8029 | 0.9514   |
  | 0.1321        | 3.0   | 6261  | 0.1413          | 0.7890    | 0.8492 | 0.8180 | 0.9546   |
  | 0.2302        | 4.0   | 8348  | 0.1326          | 0.8006    | 0.8589 | 0.8287 | 0.9562   |
  | 0.0723        | 5.0   | 10435 | 0.1290          | 0.7997    | 0.8715 | 0.8340 | 0.9574   |
  | 0.171         | 6.0   | 12522 | 0.1246          | 0.8115    | 0.8722 | 0.8408 | 0.9593   |
  | 0.1058        | 7.0   | 14609 | 0.1204          | 0.8148    | 0.8757 | 0.8441 | 0.9604   |
  | 0.1974        | 8.0   | 16696 | 0.1178          | 0.8181    | 0.8779 | 0.8470 | 0.9614   |
  | 0.0663        | 9.0   | 18783 | 0.1168          | 0.8239    | 0.8781 | 0.8501 | 0.9620   |
  | 0.1022        | 10.0  | 20870 | 0.1167          | 0.8225    | 0.8782 | 0.8494 | 0.9621   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  