readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-uncased-qqp
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        config: qqp
        split: validation
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9067029433588919
      - name: F1
        type: f1
        value: 0.8714207799290974
  ---
  
  <!-- 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-uncased-qqp
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2260
  - Accuracy: 0.9067
  - F1: 0.8714
  - Combined Score: 0.8891
  
  ## 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: 128
  - eval_batch_size: 128
  - seed: 10
  - distributed_type: multi-GPU
  - 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 | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.2922        | 1.0   | 2843  | 0.2523          | 0.8943   | 0.8604 | 0.8773         |
  | 0.1837        | 2.0   | 5686  | 0.2260          | 0.9067   | 0.8714 | 0.8891         |
  | 0.1216        | 3.0   | 8529  | 0.2612          | 0.9062   | 0.8747 | 0.8904         |
  | 0.0876        | 4.0   | 11372 | 0.2713          | 0.9084   | 0.8779 | 0.8932         |
  | 0.0669        | 5.0   | 14215 | 0.3178          | 0.9090   | 0.8770 | 0.8930         |
  | 0.0544        | 6.0   | 17058 | 0.3534          | 0.9077   | 0.8737 | 0.8907         |
  | 0.0451        | 7.0   | 19901 | 0.3821          | 0.9081   | 0.8744 | 0.8913         |
  | 0.0387        | 8.0   | 22744 | 0.4164          | 0.9101   | 0.8796 | 0.8948         |
  | 0.0336        | 9.0   | 25587 | 0.4353          | 0.9099   | 0.8790 | 0.8944         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  