readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_96
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 1.0
      - name: F1
        type: f1
        value: 1.0
  ---
  
  <!-- 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_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_96
  
  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: 0.3968
  - Accuracy: 1.0
  - F1: 1.0
  - Combined Score: 1.0
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.4718        | 1.0   | 980   | 0.4083          | 0.875    | 0.8998 | 0.8874         |
  | 0.4239        | 2.0   | 1960  | 0.3986          | 0.9902   | 0.9928 | 0.9915         |
  | 0.4182        | 3.0   | 2940  | 0.3981          | 0.9877   | 0.9910 | 0.9894         |
  | 0.4155        | 4.0   | 3920  | 0.3975          | 0.9828   | 0.9873 | 0.9851         |
  | 0.4146        | 5.0   | 4900  | 0.3969          | 0.9902   | 0.9928 | 0.9915         |
  | 0.4136        | 6.0   | 5880  | 0.3970          | 0.9853   | 0.9891 | 0.9872         |
  | 0.4131        | 7.0   | 6860  | 0.3970          | 0.9926   | 0.9946 | 0.9936         |
  | 0.4127        | 8.0   | 7840  | 0.3968          | 1.0      | 1.0    | 1.0            |
  | 0.4123        | 9.0   | 8820  | 0.3970          | 1.0      | 1.0    | 1.0            |
  | 0.412         | 10.0  | 9800  | 0.3969          | 1.0      | 1.0    | 1.0            |
  | 0.4119        | 11.0  | 10780 | 0.3969          | 0.9926   | 0.9946 | 0.9936         |
  | 0.4116        | 12.0  | 11760 | 0.3968          | 0.9902   | 0.9928 | 0.9915         |
  | 0.4116        | 13.0  | 12740 | 0.3968          | 0.9951   | 0.9964 | 0.9958         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  