readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: BERT
    results: []
  ---
  
  <!-- 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. -->
  
  # BERT
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8223
  - Accuracy: 0.82
  - Precision: 0.84
  - Recall: 0.9130
  - F1: 0.8750
  
  ## 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.6778        | 1.0   | 50   | 0.6148          | 0.69     | 0.7794    | 0.7681 | 0.7737 |
  | 0.5331        | 2.0   | 100  | 0.5578          | 0.8      | 0.8267    | 0.8986 | 0.8611 |
  | 0.3768        | 3.0   | 150  | 0.5052          | 0.73     | 0.8889    | 0.6957 | 0.7805 |
  | 0.2802        | 4.0   | 200  | 0.4998          | 0.86     | 0.8667    | 0.9420 | 0.9028 |
  | 0.1869        | 5.0   | 250  | 0.5187          | 0.81     | 0.8906    | 0.8261 | 0.8571 |
  | 0.1293        | 6.0   | 300  | 0.6516          | 0.85     | 0.8649    | 0.9275 | 0.8951 |
  | 0.1165        | 7.0   | 350  | 0.6541          | 0.82     | 0.8806    | 0.8551 | 0.8676 |
  | 0.0937        | 8.0   | 400  | 0.6855          | 0.84     | 0.8841    | 0.8841 | 0.8841 |
  | 0.0791        | 9.0   | 450  | 0.7652          | 0.81     | 0.8472    | 0.8841 | 0.8652 |
  | 0.0599        | 10.0  | 500  | 0.8223          | 0.82     | 0.84      | 0.9130 | 0.8750 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  