readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_qqp_384
    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: 0.8082364580756863
      - name: F1
        type: f1
        value: 0.7405200977275009
  ---
  
  <!-- 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_384
  
  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: 0.4322
  - Accuracy: 0.8082
  - F1: 0.7405
  - Combined Score: 0.7744
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5251        | 1.0   | 1422  | 0.5016          | 0.7563   | 0.6686 | 0.7124         |
  | 0.466         | 2.0   | 2844  | 0.4668          | 0.7745   | 0.6459 | 0.7102         |
  | 0.4292        | 3.0   | 4266  | 0.4609          | 0.7854   | 0.6685 | 0.7270         |
  | 0.3971        | 4.0   | 5688  | 0.4463          | 0.7945   | 0.7190 | 0.7568         |
  | 0.3677        | 5.0   | 7110  | 0.4326          | 0.8001   | 0.7280 | 0.7641         |
  | 0.3398        | 6.0   | 8532  | 0.4511          | 0.8017   | 0.7361 | 0.7689         |
  | 0.3141        | 7.0   | 9954  | 0.4322          | 0.8082   | 0.7405 | 0.7744         |
  | 0.2891        | 8.0   | 11376 | 0.4373          | 0.8096   | 0.7434 | 0.7765         |
  | 0.266         | 9.0   | 12798 | 0.4793          | 0.7966   | 0.7440 | 0.7703         |
  | 0.2433        | 10.0  | 14220 | 0.5018          | 0.8143   | 0.7503 | 0.7823         |
  | 0.2235        | 11.0  | 15642 | 0.4917          | 0.8144   | 0.7428 | 0.7786         |
  | 0.2045        | 12.0  | 17064 | 0.5152          | 0.8166   | 0.7521 | 0.7844         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  