readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - pv_dataset
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: test_ner3
    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: 0.6698151950718686
      - name: Recall
        type: recall
        value: 0.6499117663801446
      - name: F1
        type: f1
        value: 0.6597133941985438
      - name: Accuracy
        type: accuracy
        value: 0.9606609586670052
  ---
  
  <!-- 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. -->
  
  # test_ner3
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2983
  - Precision: 0.6698
  - Recall: 0.6499
  - F1: 0.6597
  - Accuracy: 0.9607
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1106        | 1.0   | 1813  | 0.1128          | 0.6050    | 0.5949 | 0.5999 | 0.9565   |
  | 0.0705        | 2.0   | 3626  | 0.1190          | 0.6279    | 0.6122 | 0.6200 | 0.9585   |
  | 0.0433        | 3.0   | 5439  | 0.1458          | 0.6342    | 0.5983 | 0.6157 | 0.9574   |
  | 0.0301        | 4.0   | 7252  | 0.1453          | 0.6305    | 0.6818 | 0.6552 | 0.9594   |
  | 0.0196        | 5.0   | 9065  | 0.1672          | 0.6358    | 0.6871 | 0.6605 | 0.9594   |
  | 0.0133        | 6.0   | 10878 | 0.1931          | 0.6427    | 0.6138 | 0.6279 | 0.9587   |
  | 0.0104        | 7.0   | 12691 | 0.1948          | 0.6657    | 0.6511 | 0.6583 | 0.9607   |
  | 0.0081        | 8.0   | 14504 | 0.2243          | 0.6341    | 0.6574 | 0.6455 | 0.9586   |
  | 0.0054        | 9.0   | 16317 | 0.2432          | 0.6547    | 0.6318 | 0.6431 | 0.9588   |
  | 0.0041        | 10.0  | 18130 | 0.2422          | 0.6717    | 0.6397 | 0.6553 | 0.9605   |
  | 0.0041        | 11.0  | 19943 | 0.2415          | 0.6571    | 0.6420 | 0.6495 | 0.9601   |
  | 0.0027        | 12.0  | 21756 | 0.2567          | 0.6560    | 0.6590 | 0.6575 | 0.9601   |
  | 0.0023        | 13.0  | 23569 | 0.2609          | 0.6640    | 0.6495 | 0.6566 | 0.9606   |
  | 0.002         | 14.0  | 25382 | 0.2710          | 0.6542    | 0.6670 | 0.6606 | 0.9598   |
  | 0.0012        | 15.0  | 27195 | 0.2766          | 0.6692    | 0.6539 | 0.6615 | 0.9610   |
  | 0.001         | 16.0  | 29008 | 0.2938          | 0.6692    | 0.6415 | 0.6551 | 0.9603   |
  | 0.0007        | 17.0  | 30821 | 0.2969          | 0.6654    | 0.6490 | 0.6571 | 0.9604   |
  | 0.0007        | 18.0  | 32634 | 0.3035          | 0.6628    | 0.6456 | 0.6541 | 0.9601   |
  | 0.0007        | 19.0  | 34447 | 0.2947          | 0.6730    | 0.6489 | 0.6607 | 0.9609   |
  | 0.0004        | 20.0  | 36260 | 0.2983          | 0.6698    | 0.6499 | 0.6597 | 0.9607   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  