readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-finetuned-mrpc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8602941176470589
      - name: F1
        type: f1
        value: 0.9032258064516129
    - task:
        type: natural-language-inference
        name: Natural Language Inference
      dataset:
        name: glue
        type: glue
        config: mrpc
        split: validation
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8602941176470589
        verified: true
      - name: Precision
        type: precision
        value: 0.8580645161290322
        verified: true
      - name: Recall
        type: recall
        value: 0.953405017921147
        verified: true
      - name: AUC
        type: auc
        value: 0.9257731099441527
        verified: true
      - name: F1
        type: f1
        value: 0.9032258064516129
        verified: true
      - name: loss
        type: loss
        value: 0.5150377154350281
        verified: true
  ---
  
  <!-- 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-mrpc
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5152
  - Accuracy: 0.8603
  - F1: 0.9032
  - Combined Score: 0.8818
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 16
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | No log        | 1.0   | 230  | 0.3668          | 0.8431   | 0.8881 | 0.8656         |
  | No log        | 2.0   | 460  | 0.3751          | 0.8578   | 0.9017 | 0.8798         |
  | 0.4264        | 3.0   | 690  | 0.5152          | 0.8603   | 0.9032 | 0.8818         |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.8.1+cu111
  - Datasets 1.10.3.dev0
  - Tokenizers 0.10.3
  