readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_add_GLUE_Experiment_logit_kd_pretrain_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: 0.6518426910709869
      - name: F1
        type: f1
        value: 0.11181221605249873
  ---
  
  <!-- 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_logit_kd_pretrain_qqp
  
  This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6175
  - Accuracy: 0.6518
  - F1: 0.1118
  - Combined Score: 0.3818
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.7332        | 1.0   | 1422  | 0.6640          | 0.6318   | 0.0    | 0.3159         |
  | 0.6295        | 2.0   | 2844  | 0.6362          | 0.6446   | 0.0739 | 0.3593         |
  | 0.5849        | 3.0   | 4266  | 0.6227          | 0.6401   | 0.0478 | 0.3439         |
  | 0.5561        | 4.0   | 5688  | 0.6212          | 0.6517   | 0.1097 | 0.3807         |
  | 0.5343        | 5.0   | 7110  | 0.6175          | 0.6518   | 0.1118 | 0.3818         |
  | 0.52          | 6.0   | 8532  | 0.6184          | 0.6487   | 0.0949 | 0.3718         |
  | 0.5093        | 7.0   | 9954  | 0.6239          | 0.6588   | 0.1472 | 0.4030         |
  | 0.501         | 8.0   | 11376 | 0.6235          | 0.6649   | 0.1783 | 0.4216         |
  | 0.4947        | 9.0   | 12798 | 0.6211          | 0.6622   | 0.1632 | 0.4127         |
  | 0.4895        | 10.0  | 14220 | 0.6256          | 0.6688   | 0.1970 | 0.4329         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  