readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_qqp_192
    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: 0.790972050457581
      - name: F1
        type: f1
        value: 0.7234348921687338
  ---
  
  <!-- 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_qqp_192
  
  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: 0.4568
  - Accuracy: 0.7910
  - F1: 0.7234
  - Combined Score: 0.7572
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5339        | 1.0   | 1422  | 0.5031          | 0.7551   | 0.6484 | 0.7018         |
  | 0.4835        | 2.0   | 2844  | 0.4866          | 0.7650   | 0.6504 | 0.7077         |
  | 0.4587        | 3.0   | 4266  | 0.4792          | 0.7694   | 0.6422 | 0.7058         |
  | 0.4369        | 4.0   | 5688  | 0.4851          | 0.7745   | 0.6716 | 0.7230         |
  | 0.4155        | 5.0   | 7110  | 0.4705          | 0.7791   | 0.6970 | 0.7380         |
  | 0.3961        | 6.0   | 8532  | 0.4633          | 0.7858   | 0.7093 | 0.7476         |
  | 0.3772        | 7.0   | 9954  | 0.4572          | 0.7908   | 0.7176 | 0.7542         |
  | 0.3593        | 8.0   | 11376 | 0.4568          | 0.7910   | 0.7234 | 0.7572         |
  | 0.3422        | 9.0   | 12798 | 0.4661          | 0.7927   | 0.7227 | 0.7577         |
  | 0.3265        | 10.0  | 14220 | 0.4596          | 0.7983   | 0.7290 | 0.7636         |
  | 0.3119        | 11.0  | 15642 | 0.4635          | 0.7977   | 0.7255 | 0.7616         |
  | 0.2961        | 12.0  | 17064 | 0.4857          | 0.8008   | 0.7309 | 0.7659         |
  | 0.2831        | 13.0  | 18486 | 0.4987          | 0.8037   | 0.7314 | 0.7676         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  