readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_add_GLUE_Experiment_logit_kd_mnli_96
    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: 0.5239015459723352
  ---
  
  <!-- 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_logit_kd_mnli_96
  
  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: 0.5576
  - Accuracy: 0.5239
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.624         | 1.0   | 1534  | 0.6178          | 0.3605   |
  | 0.6176        | 2.0   | 3068  | 0.6138          | 0.3767   |
  | 0.6139        | 3.0   | 4602  | 0.6112          | 0.3822   |
  | 0.6104        | 4.0   | 6136  | 0.6071          | 0.3977   |
  | 0.6027        | 5.0   | 7670  | 0.5978          | 0.4091   |
  | 0.5958        | 6.0   | 9204  | 0.6104          | 0.4151   |
  | 0.5877        | 7.0   | 10738 | 0.5963          | 0.4517   |
  | 0.5787        | 8.0   | 12272 | 0.6054          | 0.4627   |
  | 0.5711        | 9.0   | 13806 | 0.5753          | 0.4905   |
  | 0.5641        | 10.0  | 15340 | 0.5713          | 0.4987   |
  | 0.5583        | 11.0  | 16874 | 0.5645          | 0.5115   |
  | 0.5535        | 12.0  | 18408 | 0.5646          | 0.5117   |
  | 0.549         | 13.0  | 19942 | 0.5692          | 0.5176   |
  | 0.5456        | 14.0  | 21476 | 0.5613          | 0.5220   |
  | 0.5425        | 15.0  | 23010 | 0.5584          | 0.5302   |
  | 0.5399        | 16.0  | 24544 | 0.5641          | 0.5252   |
  | 0.5375        | 17.0  | 26078 | 0.5628          | 0.5260   |
  | 0.5353        | 18.0  | 27612 | 0.5659          | 0.5200   |
  | 0.533         | 19.0  | 29146 | 0.5676          | 0.5310   |
  | 0.5311        | 20.0  | 30680 | 0.5563          | 0.5323   |
  | 0.5291        | 21.0  | 32214 | 0.5682          | 0.5250   |
  | 0.5274        | 22.0  | 33748 | 0.5661          | 0.5282   |
  | 0.5255        | 23.0  | 35282 | 0.5673          | 0.5325   |
  | 0.5236        | 24.0  | 36816 | 0.5563          | 0.5416   |
  | 0.5219        | 25.0  | 38350 | 0.5703          | 0.5290   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  