readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-comp2
    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-finetuned-comp2
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9570
  - Precision: 0.5169
  - Recall: 0.6765
  - F1: 0.5820
  - Accuracy: 0.5820
  
  ## 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: 5e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.8434        | 1.0   | 934  | 0.7147          | 0.4475    | 0.6252 | 0.5096 | 0.5096   |
  | 0.6307        | 2.0   | 1868 | 0.5959          | 0.5058    | 0.6536 | 0.5585 | 0.5585   |
  | 0.4691        | 3.0   | 2802 | 0.6555          | 0.4761    | 0.6865 | 0.5521 | 0.5521   |
  | 0.334         | 4.0   | 3736 | 0.7211          | 0.5292    | 0.6682 | 0.5863 | 0.5863   |
  | 0.2326        | 5.0   | 4670 | 0.8046          | 0.4886    | 0.6865 | 0.5682 | 0.5682   |
  | 0.1625        | 6.0   | 5604 | 0.8650          | 0.4972    | 0.6851 | 0.5728 | 0.5728   |
  | 0.1195        | 7.0   | 6538 | 0.9570          | 0.5169    | 0.6765 | 0.5820 | 0.5820   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  