readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bertBasev2
    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. -->
  
  # bertBasev2
  
  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: 0.0328
  - Precision: 0.9539
  - Recall: 0.9707
  - F1: 0.9622
  - Accuracy: 0.9911
  
  ## 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
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.2004        | 1.0   | 1012 | 0.9504          | 0.2620    | 0.3519 | 0.3004 | 0.6856   |
  | 1.0265        | 2.0   | 2024 | 0.6205          | 0.4356    | 0.5161 | 0.4725 | 0.7956   |
  | 0.6895        | 3.0   | 3036 | 0.3269          | 0.6694    | 0.7302 | 0.6985 | 0.9044   |
  | 0.44          | 4.0   | 4048 | 0.1325          | 0.8356    | 0.9091 | 0.8708 | 0.9667   |
  | 0.2585        | 5.0   | 5060 | 0.0717          | 0.9259    | 0.9531 | 0.9393 | 0.9844   |
  | 0.1722        | 6.0   | 6072 | 0.0382          | 0.9480    | 0.9619 | 0.9549 | 0.99     |
  | 0.0919        | 7.0   | 7084 | 0.0328          | 0.9539    | 0.9707 | 0.9622 | 0.9911   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  