readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_add_GLUE_Experiment_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: 0.5259357200976403
  ---
  
  <!-- 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_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: 0.9590
  - Accuracy: 0.5259
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.096         | 1.0   | 1534  | 1.0536          | 0.4466   |
  | 1.0362        | 2.0   | 3068  | 1.0527          | 0.4579   |
  | 1.0213        | 3.0   | 4602  | 1.0341          | 0.4606   |
  | 1.0085        | 4.0   | 6136  | 1.0170          | 0.4810   |
  | 0.9971        | 5.0   | 7670  | 1.0013          | 0.4868   |
  | 0.9877        | 6.0   | 9204  | 0.9913          | 0.4950   |
  | 0.9805        | 7.0   | 10738 | 0.9872          | 0.4986   |
  | 0.9726        | 8.0   | 12272 | 0.9822          | 0.5019   |
  | 0.9658        | 9.0   | 13806 | 0.9812          | 0.5115   |
  | 0.9566        | 10.0  | 15340 | 0.9761          | 0.5179   |
  | 0.9439        | 11.0  | 16874 | 0.9650          | 0.5261   |
  | 0.9336        | 12.0  | 18408 | 0.9616          | 0.5283   |
  | 0.9232        | 13.0  | 19942 | 0.9620          | 0.5306   |
  | 0.9145        | 14.0  | 21476 | 0.9663          | 0.5329   |
  | 0.9056        | 15.0  | 23010 | 0.9491          | 0.5408   |
  | 0.8986        | 16.0  | 24544 | 0.9567          | 0.5389   |
  | 0.8903        | 17.0  | 26078 | 0.9488          | 0.5382   |
  | 0.8835        | 18.0  | 27612 | 0.9739          | 0.5202   |
  | 0.8769        | 19.0  | 29146 | 0.9761          | 0.5382   |
  | 0.8701        | 20.0  | 30680 | 0.9504          | 0.5439   |
  | 0.8633        | 21.0  | 32214 | 1.0074          | 0.5265   |
  | 0.8576        | 22.0  | 33748 | 0.9874          | 0.5392   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  