readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-new-pro
    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-base-cased-new-pro
  
  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: 0.9579
  - Precision: 0.4003
  - Recall: 0.4412
  - F1: 0.4198
  - Accuracy: 0.7728
  
  ## 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: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 180  | 0.8269          | 0.2988    | 0.4480 | 0.3585 | 0.7566   |
  | No log        | 2.0   | 360  | 0.6185          | 0.4305    | 0.5405 | 0.4793 | 0.7968   |
  | 0.934         | 3.0   | 540  | 0.5781          | 0.4428    | 0.5807 | 0.5024 | 0.8124   |
  | 0.934         | 4.0   | 720  | 0.5720          | 0.4486    | 0.6116 | 0.5175 | 0.8108   |
  | 0.934         | 5.0   | 900  | 0.5712          | 0.4739    | 0.6080 | 0.5326 | 0.8184   |
  | 0.4489        | 6.0   | 1080 | 0.5867          | 0.4732    | 0.6347 | 0.5422 | 0.8149   |
  | 0.4489        | 7.0   | 1260 | 0.6198          | 0.4618    | 0.6405 | 0.5366 | 0.8115   |
  | 0.4489        | 8.0   | 1440 | 0.6256          | 0.4701    | 0.6475 | 0.5447 | 0.8154   |
  | 0.3172        | 9.0   | 1620 | 0.6546          | 0.4477    | 0.6442 | 0.5283 | 0.8047   |
  | 0.3172        | 10.0  | 1800 | 0.6834          | 0.4603    | 0.6495 | 0.5388 | 0.8067   |
  | 0.3172        | 11.0  | 1980 | 0.7090          | 0.4818    | 0.6482 | 0.5528 | 0.8081   |
  | 0.2333        | 12.0  | 2160 | 0.7213          | 0.4871    | 0.6392 | 0.5529 | 0.8125   |
  | 0.2333        | 13.0  | 2340 | 0.7300          | 0.4785    | 0.6389 | 0.5472 | 0.8155   |
  | 0.179         | 14.0  | 2520 | 0.7775          | 0.4700    | 0.6570 | 0.5480 | 0.8090   |
  | 0.179         | 15.0  | 2700 | 0.7967          | 0.4793    | 0.6435 | 0.5494 | 0.8109   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  