readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_GLUE_Experiment_qqp_256
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        config: qqp
        split: validation
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8135790254761316
      - name: F1
        type: f1
        value: 0.7425272435349981
  ---
  
  <!-- 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_qqp_256
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4273
  - Accuracy: 0.8136
  - F1: 0.7425
  - Combined Score: 0.7781
  
  ## 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 | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5648        | 1.0   | 1422  | 0.5394          | 0.7316   | 0.6540 | 0.6928         |
  | 0.5038        | 2.0   | 2844  | 0.5093          | 0.7496   | 0.6564 | 0.7030         |
  | 0.4837        | 3.0   | 4266  | 0.4952          | 0.7623   | 0.6625 | 0.7124         |
  | 0.4624        | 4.0   | 5688  | 0.4777          | 0.7739   | 0.6844 | 0.7292         |
  | 0.4197        | 5.0   | 7110  | 0.4541          | 0.7925   | 0.6939 | 0.7432         |
  | 0.3693        | 6.0   | 8532  | 0.4539          | 0.8027   | 0.7012 | 0.7519         |
  | 0.3214        | 7.0   | 9954  | 0.4273          | 0.8136   | 0.7425 | 0.7781         |
  | 0.2804        | 8.0   | 11376 | 0.4547          | 0.8187   | 0.7344 | 0.7765         |
  | 0.2463        | 9.0   | 12798 | 0.4779          | 0.8227   | 0.7478 | 0.7852         |
  | 0.2177        | 10.0  | 14220 | 0.5060          | 0.8256   | 0.7510 | 0.7883         |
  | 0.1933        | 11.0  | 15642 | 0.5020          | 0.8272   | 0.7587 | 0.7929         |
  | 0.1741        | 12.0  | 17064 | 0.5385          | 0.8304   | 0.7604 | 0.7954         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  