readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_GLUE_Experiment_mrpc_256
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7107843137254902
      - name: F1
        type: f1
        value: 0.8233532934131738
  ---
  
  <!-- 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_add_GLUE_Experiment_mrpc_256
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5932
  - Accuracy: 0.7108
  - F1: 0.8234
  - Combined Score: 0.7671
  
  ## 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: 256
  - eval_batch_size: 256
  - 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.637         | 1.0   | 15   | 0.6242          | 0.6838   | 0.8122 | 0.7480         |
  | 0.629         | 2.0   | 30   | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6302        | 3.0   | 45   | 0.6248          | 0.6838   | 0.8122 | 0.7480         |
  | 0.63          | 4.0   | 60   | 0.6241          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6323        | 5.0   | 75   | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6299        | 6.0   | 90   | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6325        | 7.0   | 105  | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6301        | 8.0   | 120  | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6324        | 9.0   | 135  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6293        | 10.0  | 150  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6307        | 11.0  | 165  | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6302        | 12.0  | 180  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6338        | 13.0  | 195  | 0.6237          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6281        | 14.0  | 210  | 0.6225          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6263        | 15.0  | 225  | 0.6183          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6017        | 16.0  | 240  | 0.5932          | 0.7108   | 0.8234 | 0.7671         |
  | 0.5213        | 17.0  | 255  | 0.6146          | 0.6642   | 0.7540 | 0.7091         |
  | 0.4383        | 18.0  | 270  | 0.6405          | 0.6912   | 0.7842 | 0.7377         |
  | 0.3903        | 19.0  | 285  | 0.6910          | 0.6912   | 0.7872 | 0.7392         |
  | 0.363         | 20.0  | 300  | 0.7221          | 0.6544   | 0.7374 | 0.6959         |
  | 0.3306        | 21.0  | 315  | 0.7583          | 0.6863   | 0.7808 | 0.7335         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  