readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_qqp_96
    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: 0.7756863715063071
      - name: F1
        type: f1
        value: 0.6671804469888803
  ---
  
  <!-- 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_96
  
  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: 0.4818
  - Accuracy: 0.7757
  - F1: 0.6672
  - Combined Score: 0.7214
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5528        | 1.0   | 1422  | 0.5115          | 0.7528   | 0.6284 | 0.6906         |
  | 0.4965        | 2.0   | 2844  | 0.4960          | 0.7614   | 0.6420 | 0.7017         |
  | 0.4769        | 3.0   | 4266  | 0.4904          | 0.7650   | 0.6382 | 0.7016         |
  | 0.4619        | 4.0   | 5688  | 0.4901          | 0.7680   | 0.6526 | 0.7103         |
  | 0.4489        | 5.0   | 7110  | 0.4844          | 0.7709   | 0.6663 | 0.7186         |
  | 0.4366        | 6.0   | 8532  | 0.4860          | 0.7721   | 0.6712 | 0.7217         |
  | 0.425         | 7.0   | 9954  | 0.4860          | 0.7747   | 0.6636 | 0.7192         |
  | 0.414         | 8.0   | 11376 | 0.4818          | 0.7757   | 0.6672 | 0.7214         |
  | 0.4027        | 9.0   | 12798 | 0.4871          | 0.7786   | 0.6722 | 0.7254         |
  | 0.3926        | 10.0  | 14220 | 0.4919          | 0.7749   | 0.6932 | 0.7340         |
  | 0.3824        | 11.0  | 15642 | 0.4890          | 0.7801   | 0.6823 | 0.7312         |
  | 0.3718        | 12.0  | 17064 | 0.4981          | 0.7801   | 0.6970 | 0.7385         |
  | 0.3629        | 13.0  | 18486 | 0.4989          | 0.7805   | 0.6968 | 0.7386         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  