readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_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: 0.7980954736581746
      - name: F1
        type: f1
        value: 0.7243254196075782
  ---
  
  <!-- 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
  
  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: 0.4299
  - Accuracy: 0.7981
  - F1: 0.7243
  - Combined Score: 0.7612
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5166        | 1.0   | 1422  | 0.4817          | 0.7654   | 0.6882 | 0.7268         |
  | 0.4462        | 2.0   | 2844  | 0.4460          | 0.7885   | 0.6950 | 0.7417         |
  | 0.3979        | 3.0   | 4266  | 0.4299          | 0.7981   | 0.7243 | 0.7612         |
  | 0.3497        | 4.0   | 5688  | 0.4417          | 0.7972   | 0.7421 | 0.7696         |
  | 0.2994        | 5.0   | 7110  | 0.4330          | 0.8099   | 0.7495 | 0.7797         |
  | 0.2514        | 6.0   | 8532  | 0.4764          | 0.8137   | 0.7499 | 0.7818         |
  | 0.2065        | 7.0   | 9954  | 0.4819          | 0.8123   | 0.7520 | 0.7822         |
  | 0.1669        | 8.0   | 11376 | 0.5460          | 0.8132   | 0.7555 | 0.7844         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  