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_qqp_256
    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: 0.6553549344546129
      - name: F1
        type: f1
        value: 0.13097168516901583
  ---
  
  <!-- 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_qqp_256
  
  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: 0.6586
  - Accuracy: 0.6554
  - F1: 0.1310
  - Combined Score: 0.3932
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.8355        | 1.0   | 1422  | 0.8004          | 0.6318   | 0.0    | 0.3159         |
  | 0.7677        | 2.0   | 2844  | 0.7488          | 0.6318   | 0.0    | 0.3159         |
  | 0.7048        | 3.0   | 4266  | 0.6935          | 0.6318   | 0.0    | 0.3159         |
  | 0.6449        | 4.0   | 5688  | 0.6875          | 0.6337   | 0.0106 | 0.3221         |
  | 0.6082        | 5.0   | 7110  | 0.6688          | 0.6354   | 0.0205 | 0.3279         |
  | 0.5829        | 6.0   | 8532  | 0.6854          | 0.6386   | 0.0393 | 0.3389         |
  | 0.5637        | 7.0   | 9954  | 0.6707          | 0.6522   | 0.1155 | 0.3839         |
  | 0.5502        | 8.0   | 11376 | 0.6752          | 0.6522   | 0.1145 | 0.3833         |
  | 0.5389        | 9.0   | 12798 | 0.6677          | 0.6561   | 0.1348 | 0.3954         |
  | 0.5304        | 10.0  | 14220 | 0.6693          | 0.6622   | 0.1659 | 0.4140         |
  | 0.5234        | 11.0  | 15642 | 0.6728          | 0.6511   | 0.1082 | 0.3797         |
  | 0.5175        | 12.0  | 17064 | 0.6812          | 0.6554   | 0.1303 | 0.3928         |
  | 0.5127        | 13.0  | 18486 | 0.6644          | 0.6540   | 0.1235 | 0.3888         |
  | 0.5085        | 14.0  | 19908 | 0.6605          | 0.6622   | 0.1677 | 0.4149         |
  | 0.505         | 15.0  | 21330 | 0.6647          | 0.6570   | 0.1391 | 0.3980         |
  | 0.502         | 16.0  | 22752 | 0.6667          | 0.6528   | 0.1170 | 0.3849         |
  | 0.499         | 17.0  | 24174 | 0.6586          | 0.6554   | 0.1310 | 0.3932         |
  | 0.497         | 18.0  | 25596 | 0.6589          | 0.6597   | 0.1535 | 0.4066         |
  | 0.4947        | 19.0  | 27018 | 0.6715          | 0.6599   | 0.1535 | 0.4067         |
  | 0.4928        | 20.0  | 28440 | 0.6631          | 0.6535   | 0.1202 | 0.3868         |
  | 0.4907        | 21.0  | 29862 | 0.6690          | 0.6651   | 0.1796 | 0.4224         |
  | 0.4891        | 22.0  | 31284 | 0.6603          | 0.6652   | 0.1830 | 0.4241         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  