readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_GLUE_Experiment_mrpc_384
    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: 0.7009803921568627
      - name: F1
        type: f1
        value: 0.8189910979228486
  ---
  
  <!-- 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_384
  
  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: 0.5935
  - Accuracy: 0.7010
  - F1: 0.8190
  - Combined Score: 0.7600
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6355        | 1.0   | 15   | 0.6261          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6315        | 2.0   | 30   | 0.6294          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6327        | 3.0   | 45   | 0.6241          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6344        | 4.0   | 60   | 0.6285          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6328        | 5.0   | 75   | 0.6245          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6293        | 6.0   | 90   | 0.6245          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6341        | 7.0   | 105  | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6298        | 8.0   | 120  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6304        | 9.0   | 135  | 0.6232          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6286        | 10.0  | 150  | 0.6196          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6045        | 11.0  | 165  | 0.5935          | 0.7010   | 0.8190 | 0.7600         |
  | 0.5251        | 12.0  | 180  | 0.6129          | 0.6789   | 0.7849 | 0.7319         |
  | 0.4395        | 13.0  | 195  | 0.6564          | 0.6912   | 0.7872 | 0.7392         |
  | 0.3921        | 14.0  | 210  | 0.7059          | 0.6446   | 0.7173 | 0.6810         |
  | 0.3399        | 15.0  | 225  | 0.7605          | 0.6887   | 0.7829 | 0.7358         |
  | 0.3219        | 16.0  | 240  | 0.7614          | 0.6569   | 0.7328 | 0.6948         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  