readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_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: 0.7997031906999753
      - name: F1
        type: f1
        value: 0.7305158069883527
  ---
  
  <!-- 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_add_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: 0.4419
  - Accuracy: 0.7997
  - F1: 0.7305
  - Combined Score: 0.7651
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5641        | 1.0   | 1422  | 0.5422          | 0.7304   | 0.6553 | 0.6929         |
  | 0.5047        | 2.0   | 2844  | 0.5128          | 0.7504   | 0.6575 | 0.7039         |
  | 0.487         | 3.0   | 4266  | 0.5020          | 0.7573   | 0.6656 | 0.7114         |
  | 0.4729        | 4.0   | 5688  | 0.4907          | 0.7638   | 0.6695 | 0.7166         |
  | 0.4502        | 5.0   | 7110  | 0.4759          | 0.7789   | 0.6736 | 0.7262         |
  | 0.4139        | 6.0   | 8532  | 0.4635          | 0.7926   | 0.6909 | 0.7417         |
  | 0.3728        | 7.0   | 9954  | 0.4419          | 0.7997   | 0.7305 | 0.7651         |
  | 0.3334        | 8.0   | 11376 | 0.4760          | 0.8026   | 0.6964 | 0.7495         |
  | 0.2998        | 9.0   | 12798 | 0.4597          | 0.8125   | 0.7314 | 0.7719         |
  | 0.2704        | 10.0  | 14220 | 0.4692          | 0.8173   | 0.7403 | 0.7788         |
  | 0.244         | 11.0  | 15642 | 0.4990          | 0.8195   | 0.7413 | 0.7804         |
  | 0.2218        | 12.0  | 17064 | 0.5195          | 0.8198   | 0.7350 | 0.7774         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  