readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-uncased-finetuned-mrpc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8676470588235294
      - name: F1
        type: f1
        value: 0.9045936395759717
  ---
  
  <!-- 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-finetuned-mrpc
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5500
  - Accuracy: 0.8676
  - F1: 0.9046
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | No log        | 1.0   | 230  | 0.3669          | 0.8309   | 0.8796 |
  | No log        | 2.0   | 460  | 0.3704          | 0.8652   | 0.9076 |
  | 0.3951        | 3.0   | 690  | 0.4974          | 0.8627   | 0.9041 |
  | 0.3951        | 4.0   | 920  | 0.5454          | 0.8652   | 0.9053 |
  | 0.0994        | 5.0   | 1150 | 0.5500          | 0.8676   | 0.9046 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  