readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_add_GLUE_Experiment_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: 0.500406834825061
  ---
  
  <!-- 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_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: 1.0256
  - Accuracy: 0.5004
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0987        | 1.0   | 1534  | 1.0980          | 0.3545   |
  | 1.0979        | 2.0   | 3068  | 1.0942          | 0.3580   |
  | 1.0897        | 3.0   | 4602  | 1.0896          | 0.3706   |
  | 1.0817        | 4.0   | 6136  | 1.0769          | 0.3991   |
  | 1.072         | 5.0   | 7670  | 1.0680          | 0.4146   |
  | 1.0603        | 6.0   | 9204  | 1.0700          | 0.4174   |
  | 1.0515        | 7.0   | 10738 | 1.0655          | 0.4179   |
  | 1.0441        | 8.0   | 12272 | 1.0546          | 0.4335   |
  | 1.038         | 9.0   | 13806 | 1.0751          | 0.4059   |
  | 1.0344        | 10.0  | 15340 | 1.0554          | 0.4363   |
  | 1.0275        | 11.0  | 16874 | 1.0736          | 0.4207   |
  | 1.0225        | 12.0  | 18408 | 1.0662          | 0.4295   |
  | 1.0169        | 13.0  | 19942 | 1.0544          | 0.4421   |
  | 1.0111        | 14.0  | 21476 | 1.0635          | 0.4411   |
  | 1.0043        | 15.0  | 23010 | 1.0505          | 0.4567   |
  | 0.9986        | 16.0  | 24544 | 1.0402          | 0.4643   |
  | 0.9925        | 17.0  | 26078 | 1.0531          | 0.4545   |
  | 0.9861        | 18.0  | 27612 | 1.0431          | 0.4675   |
  | 0.9781        | 19.0  | 29146 | 1.0361          | 0.4801   |
  | 0.9673        | 20.0  | 30680 | 1.0301          | 0.4879   |
  | 0.9552        | 21.0  | 32214 | 1.0327          | 0.4908   |
  | 0.9467        | 22.0  | 33748 | 1.0248          | 0.5013   |
  | 0.9396        | 23.0  | 35282 | 1.0297          | 0.4977   |
  | 0.9328        | 24.0  | 36816 | 1.0237          | 0.5025   |
  | 0.9277        | 25.0  | 38350 | 1.0384          | 0.5010   |
  | 0.9228        | 26.0  | 39884 | 1.0374          | 0.5037   |
  | 0.918         | 27.0  | 41418 | 1.0242          | 0.5006   |
  | 0.9128        | 28.0  | 42952 | 1.0248          | 0.5060   |
  | 0.9087        | 29.0  | 44486 | 1.0283          | 0.5027   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  