readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_sa_GLUE_Experiment_data_aug_qnli_256
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.5680029287937031
  ---
  
  <!-- 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_data_aug_qnli_256
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0586
  - Accuracy: 0.5680
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.4745        | 1.0   | 16604 | 1.0586          | 0.5680   |
  | 0.2251        | 2.0   | 33208 | 1.3085          | 0.5707   |
  | 0.1318        | 3.0   | 49812 | 1.4267          | 0.5808   |
  | 0.0838        | 4.0   | 66416 | 1.6667          | 0.5779   |
  | 0.0572        | 5.0   | 83020 | 1.9572          | 0.5764   |
  | 0.041         | 6.0   | 99624 | 2.0602          | 0.5744   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  