readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_add_GLUE_Experiment_mnli_384
    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: 0.5652969894222946
  ---
  
  <!-- 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_384
  
  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: 0.9341
  - Accuracy: 0.5653
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.074         | 1.0   | 1534  | 1.0374          | 0.4592   |
  | 1.0289        | 2.0   | 3068  | 1.0214          | 0.4736   |
  | 1.0052        | 3.0   | 4602  | 0.9957          | 0.4889   |
  | 0.9869        | 4.0   | 6136  | 0.9924          | 0.5111   |
  | 0.9709        | 5.0   | 7670  | 0.9683          | 0.5246   |
  | 0.9562        | 6.0   | 9204  | 0.9628          | 0.5295   |
  | 0.9403        | 7.0   | 10738 | 0.9524          | 0.5379   |
  | 0.9244        | 8.0   | 12272 | 0.9547          | 0.5309   |
  | 0.9071        | 9.0   | 13806 | 0.9358          | 0.5563   |
  | 0.8914        | 10.0  | 15340 | 0.9286          | 0.5586   |
  | 0.8771        | 11.0  | 16874 | 0.9384          | 0.5651   |
  | 0.863         | 12.0  | 18408 | 0.9361          | 0.5616   |
  | 0.849         | 13.0  | 19942 | 0.9401          | 0.5606   |
  | 0.8346        | 14.0  | 21476 | 0.9441          | 0.5646   |
  | 0.8199        | 15.0  | 23010 | 0.9276          | 0.5677   |
  | 0.8048        | 16.0  | 24544 | 0.9444          | 0.5656   |
  | 0.7883        | 17.0  | 26078 | 0.9477          | 0.5653   |
  | 0.7707        | 18.0  | 27612 | 0.9491          | 0.5745   |
  | 0.7543        | 19.0  | 29146 | 0.9613          | 0.5825   |
  | 0.7371        | 20.0  | 30680 | 0.9645          | 0.5728   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  