readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_GLUE_Experiment_qqp
    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: 0.8319564679693298
      - name: F1
        type: f1
        value: 0.7639168809507263
  ---
  
  <!-- 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
  
  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: 0.4050
  - Accuracy: 0.8320
  - F1: 0.7639
  - Combined Score: 0.7979
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5406        | 1.0   | 1422  | 0.4844          | 0.7648   | 0.6276 | 0.6962         |
  | 0.4161        | 2.0   | 2844  | 0.4451          | 0.8044   | 0.6939 | 0.7491         |
  | 0.3079        | 3.0   | 4266  | 0.4050          | 0.8320   | 0.7639 | 0.7979         |
  | 0.2338        | 4.0   | 5688  | 0.4633          | 0.8388   | 0.7715 | 0.8052         |
  | 0.1801        | 5.0   | 7110  | 0.5597          | 0.8346   | 0.7489 | 0.7918         |
  | 0.1433        | 6.0   | 8532  | 0.5641          | 0.8460   | 0.7774 | 0.8117         |
  | 0.1155        | 7.0   | 9954  | 0.5940          | 0.8481   | 0.7889 | 0.8185         |
  | 0.0963        | 8.0   | 11376 | 0.6896          | 0.8438   | 0.7670 | 0.8054         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  