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
    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: 0.6524613506916193
  ---
  
  <!-- 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
  
  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: 0.4989
  - Accuracy: 0.6525
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.575         | 1.0   | 1534  | 0.5428          | 0.5554   |
  | 0.5345        | 2.0   | 3068  | 0.5205          | 0.5987   |
  | 0.511         | 3.0   | 4602  | 0.5105          | 0.6222   |
  | 0.4917        | 4.0   | 6136  | 0.5021          | 0.6360   |
  | 0.4735        | 5.0   | 7670  | 0.5004          | 0.6470   |
  | 0.4557        | 6.0   | 9204  | 0.4976          | 0.6534   |
  | 0.4391        | 7.0   | 10738 | 0.4982          | 0.6606   |
  | 0.4231        | 8.0   | 12272 | 0.4982          | 0.6586   |
  | 0.4082        | 9.0   | 13806 | 0.5020          | 0.6587   |
  | 0.394         | 10.0  | 15340 | 0.5082          | 0.6561   |
  | 0.3816        | 11.0  | 16874 | 0.5140          | 0.6617   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  