readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_mnli_192
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MNLI
        type: glue
        config: mnli
        split: validation_matched
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.57740032546786
  ---
  
  <!-- 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_mnli_192
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5326
  - Accuracy: 0.5774
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.5885        | 1.0   | 1534  | 0.5621          | 0.5096   |
  | 0.5572        | 2.0   | 3068  | 0.5481          | 0.5303   |
  | 0.5456        | 3.0   | 4602  | 0.5473          | 0.5347   |
  | 0.5373        | 4.0   | 6136  | 0.5404          | 0.5533   |
  | 0.5299        | 5.0   | 7670  | 0.5371          | 0.5629   |
  | 0.5235        | 6.0   | 9204  | 0.5361          | 0.5671   |
  | 0.5172        | 7.0   | 10738 | 0.5360          | 0.5645   |
  | 0.5114        | 8.0   | 12272 | 0.5391          | 0.5664   |
  | 0.5058        | 9.0   | 13806 | 0.5332          | 0.5839   |
  | 0.5004        | 10.0  | 15340 | 0.5294          | 0.5867   |
  | 0.4951        | 11.0  | 16874 | 0.5284          | 0.5905   |
  | 0.4901        | 12.0  | 18408 | 0.5309          | 0.5892   |
  | 0.4853        | 13.0  | 19942 | 0.5334          | 0.5967   |
  | 0.4804        | 14.0  | 21476 | 0.5344          | 0.5954   |
  | 0.4754        | 15.0  | 23010 | 0.5297          | 0.5987   |
  | 0.4707        | 16.0  | 24544 | 0.5348          | 0.5989   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  