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_256
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9901960784313726
      - name: F1
        type: f1
        value: 0.9927797833935018
  ---
  
  <!-- 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_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: 0.3967
  - Accuracy: 0.9902
  - F1: 0.9928
  - Combined Score: 0.9915
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.4491        | 1.0   | 980  | 0.4030          | 0.9632   | 0.9727 | 0.9680         |
  | 0.4186        | 2.0   | 1960 | 0.4002          | 0.9730   | 0.9800 | 0.9765         |
  | 0.4153        | 3.0   | 2940 | 0.3983          | 0.9926   | 0.9946 | 0.9936         |
  | 0.4139        | 4.0   | 3920 | 0.3967          | 0.9902   | 0.9928 | 0.9915         |
  | 0.413         | 5.0   | 4900 | 0.3968          | 1.0      | 1.0    | 1.0            |
  | 0.4125        | 6.0   | 5880 | 0.3970          | 0.9877   | 0.9910 | 0.9894         |
  | 0.4121        | 7.0   | 6860 | 0.3971          | 1.0      | 1.0    | 1.0            |
  | 0.4118        | 8.0   | 7840 | 0.3968          | 0.9926   | 0.9946 | 0.9936         |
  | 0.4116        | 9.0   | 8820 | 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
  