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
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8946078431372549
      - name: F1
        type: f1
        value: 0.916504854368932
  ---
  
  <!-- 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
  
  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: 0.3959
  - Accuracy: 0.8946
  - F1: 0.9165
  - Combined Score: 0.9056
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.4396        | 1.0   | 980   | 0.4005          | 0.9975   | 0.9982 | 0.9979         |
  | 0.4146        | 2.0   | 1960  | 0.3981          | 0.9853   | 0.9892 | 0.9872         |
  | 0.413         | 3.0   | 2940  | 0.3971          | 1.0      | 1.0    | 1.0            |
  | 0.4119        | 4.0   | 3920  | 0.3968          | 0.9902   | 0.9928 | 0.9915         |
  | 0.4114        | 5.0   | 4900  | 0.3966          | 1.0      | 1.0    | 1.0            |
  | 0.4112        | 6.0   | 5880  | 0.3966          | 0.9951   | 0.9964 | 0.9958         |
  | 0.4108        | 7.0   | 6860  | 0.3966          | 1.0      | 1.0    | 1.0            |
  | 0.4107        | 8.0   | 7840  | 0.3962          | 0.9877   | 0.9910 | 0.9894         |
  | 0.4105        | 9.0   | 8820  | 0.3962          | 0.9902   | 0.9928 | 0.9915         |
  | 0.4104        | 10.0  | 9800  | 0.3967          | 0.9020   | 0.9228 | 0.9124         |
  | 0.4102        | 11.0  | 10780 | 0.3964          | 0.8971   | 0.9186 | 0.9078         |
  | 0.4102        | 12.0  | 11760 | 0.3963          | 0.9975   | 0.9982 | 0.9979         |
  | 0.4103        | 13.0  | 12740 | 0.3962          | 0.8431   | 0.8704 | 0.8568         |
  | 0.4102        | 14.0  | 13720 | 0.3967          | 0.7966   | 0.8253 | 0.8109         |
  | 0.4101        | 15.0  | 14700 | 0.3962          | 0.8971   | 0.9186 | 0.9078         |
  | 0.4101        | 16.0  | 15680 | 0.3959          | 0.8946   | 0.9165 | 0.9056         |
  | 0.41          | 17.0  | 16660 | 0.3963          | 0.8848   | 0.9080 | 0.8964         |
  | 0.41          | 18.0  | 17640 | 0.3960          | 0.8676   | 0.8929 | 0.8803         |
  | 0.41          | 19.0  | 18620 | 0.3962          | 0.8186   | 0.8471 | 0.8329         |
  | 0.4101        | 20.0  | 19600 | 0.3959          | 0.8848   | 0.9080 | 0.8964         |
  | 0.41          | 21.0  | 20580 | 0.3959          | 0.8799   | 0.9037 | 0.8918         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  