readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_add_GLUE_Experiment_mnli_256
    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: 0.5455655004068348
  ---
  
  <!-- 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_256
  
  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: 0.9622
  - Accuracy: 0.5456
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0905        | 1.0   | 1534  | 1.0415          | 0.4633   |
  | 1.0344        | 2.0   | 3068  | 1.0571          | 0.4569   |
  | 1.0137        | 3.0   | 4602  | 1.0080          | 0.4888   |
  | 0.9902        | 4.0   | 6136  | 0.9876          | 0.5090   |
  | 0.9733        | 5.0   | 7670  | 0.9744          | 0.5134   |
  | 0.9596        | 6.0   | 9204  | 0.9726          | 0.5138   |
  | 0.9484        | 7.0   | 10738 | 0.9684          | 0.5233   |
  | 0.938         | 8.0   | 12272 | 0.9715          | 0.5150   |
  | 0.9279        | 9.0   | 13806 | 0.9707          | 0.5237   |
  | 0.9188        | 10.0  | 15340 | 0.9580          | 0.5319   |
  | 0.9102        | 11.0  | 16874 | 0.9561          | 0.5396   |
  | 0.9011        | 12.0  | 18408 | 0.9594          | 0.5368   |
  | 0.893         | 13.0  | 19942 | 0.9641          | 0.5345   |
  | 0.8845        | 14.0  | 21476 | 0.9671          | 0.5367   |
  | 0.8751        | 15.0  | 23010 | 0.9553          | 0.5388   |
  | 0.8662        | 16.0  | 24544 | 0.9601          | 0.5447   |
  | 0.8573        | 17.0  | 26078 | 0.9519          | 0.5473   |
  | 0.849         | 18.0  | 27612 | 0.9814          | 0.5410   |
  | 0.8404        | 19.0  | 29146 | 0.9733          | 0.5464   |
  | 0.832         | 20.0  | 30680 | 0.9671          | 0.5492   |
  | 0.8233        | 21.0  | 32214 | 0.9864          | 0.5509   |
  | 0.8144        | 22.0  | 33748 | 0.9925          | 0.5536   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  