readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_data_aug_qqp_192
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7887212465990601
      - name: F1
        type: f1
        value: 0.7232374287195439
  ---
  
  <!-- 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_data_aug_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: 0.4878
  - Accuracy: 0.7887
  - F1: 0.7232
  - Combined Score: 0.7560
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
  | 0.4161        | 1.0   | 29671  | 0.4878          | 0.7887   | 0.7232 | 0.7560         |
  | 0.2684        | 2.0   | 59342  | 0.5351          | 0.7965   | 0.7274 | 0.7619         |
  | 0.1914        | 3.0   | 89013  | 0.6132          | 0.7992   | 0.7324 | 0.7658         |
  | 0.1466        | 4.0   | 118684 | 0.6588          | 0.7999   | 0.7350 | 0.7674         |
  | 0.1178        | 5.0   | 148355 | 0.7657          | 0.7983   | 0.7338 | 0.7661         |
  | 0.0978        | 6.0   | 178026 | 0.7679          | 0.8040   | 0.7313 | 0.7677         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  