readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - pv_dataset
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: PV-Bio_clinicalBERT-superset
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: pv_dataset
        type: pv_dataset
        config: PVDatasetCorpus
        split: train
        args: PVDatasetCorpus
      metrics:
      - name: Precision
        type: precision
        value: 0.7055946686730801
      - name: Recall
        type: recall
        value: 0.7473672226333467
      - name: F1
        type: f1
        value: 0.7258804666334938
      - name: Accuracy
        type: accuracy
        value: 0.9656573815513143
  ---
  
  <!-- 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. -->
  
  # PV-Bio_clinicalBERT-superset
  
  This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingface.co/giacomomiolo/electramed_base_scivocab_1M) on the pv_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2082
  - Precision: 0.7056
  - Recall: 0.7474
  - F1: 0.7259
  - Accuracy: 0.9657
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.063         | 1.0   | 1813  | 0.1061          | 0.6453    | 0.7306 | 0.6853 | 0.9623   |
  | 0.0086        | 2.0   | 3626  | 0.1068          | 0.6620    | 0.7516 | 0.7040 | 0.9647   |
  | 0.0089        | 3.0   | 5439  | 0.1265          | 0.7026    | 0.7300 | 0.7160 | 0.9657   |
  | 0.004         | 4.0   | 7252  | 0.1369          | 0.6820    | 0.7601 | 0.7189 | 0.9638   |
  | 0.0004        | 5.0   | 9065  | 0.1573          | 0.6937    | 0.7602 | 0.7254 | 0.9656   |
  | 0.0184        | 6.0   | 10878 | 0.1707          | 0.7078    | 0.7475 | 0.7271 | 0.9662   |
  | 0.0009        | 7.0   | 12691 | 0.1787          | 0.7116    | 0.7398 | 0.7254 | 0.9662   |
  | 0.0006        | 8.0   | 14504 | 0.1874          | 0.6979    | 0.7576 | 0.7265 | 0.9655   |
  | 0.0008        | 9.0   | 16317 | 0.1970          | 0.7083    | 0.7475 | 0.7273 | 0.9660   |
  | 0.0003        | 10.0  | 18130 | 0.2082          | 0.7056    | 0.7474 | 0.7259 | 0.9657   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  