readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert-mrpc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8480392156862745
      - name: F1
        type: f1
        value: 0.8934707903780068
  ---
  
  <!-- 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. -->
  
  # distilbert-mrpc
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6783
  - Accuracy: 0.8480
  - F1: 0.8935
  
  ## 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
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.5916        | 0.22  | 100  | 0.5676          | 0.7157   | 0.8034 |
  | 0.5229        | 0.44  | 200  | 0.4534          | 0.7770   | 0.8212 |
  | 0.5055        | 0.65  | 300  | 0.4037          | 0.8137   | 0.8762 |
  | 0.4597        | 0.87  | 400  | 0.3706          | 0.8407   | 0.8893 |
  | 0.4           | 1.09  | 500  | 0.4590          | 0.8113   | 0.8566 |
  | 0.3498        | 1.31  | 600  | 0.4196          | 0.8554   | 0.8974 |
  | 0.2916        | 1.53  | 700  | 0.4606          | 0.8554   | 0.8933 |
  | 0.3309        | 1.74  | 800  | 0.5162          | 0.8578   | 0.9027 |
  | 0.3788        | 1.96  | 900  | 0.3911          | 0.8529   | 0.8980 |
  | 0.2059        | 2.18  | 1000 | 0.5842          | 0.8554   | 0.8995 |
  | 0.1595        | 2.4   | 1100 | 0.5701          | 0.8578   | 0.8975 |
  | 0.1205        | 2.61  | 1200 | 0.6905          | 0.8407   | 0.8889 |
  | 0.174         | 2.83  | 1300 | 0.6783          | 0.8480   | 0.8935 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  