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_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: 0.6416522384368043
      - name: F1
        type: f1
        value: 0.057507155867811605
  ---
  
  <!-- 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_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: 0.6704
  - Accuracy: 0.6417
  - F1: 0.0575
  - Combined Score: 0.3496
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.8489        | 1.0   | 1422  | 0.8086          | 0.6318   | 0.0    | 0.3159         |
  | 0.776         | 2.0   | 2844  | 0.7797          | 0.6318   | 0.0    | 0.3159         |
  | 0.7501        | 3.0   | 4266  | 0.7462          | 0.6318   | 0.0    | 0.3159         |
  | 0.7008        | 4.0   | 5688  | 0.7089          | 0.6318   | 0.0    | 0.3159         |
  | 0.6547        | 5.0   | 7110  | 0.7026          | 0.6318   | 0.0    | 0.3159         |
  | 0.6221        | 6.0   | 8532  | 0.6962          | 0.6338   | 0.0115 | 0.3226         |
  | 0.5981        | 7.0   | 9954  | 0.6812          | 0.6437   | 0.0693 | 0.3565         |
  | 0.5797        | 8.0   | 11376 | 0.6846          | 0.6361   | 0.0261 | 0.3311         |
  | 0.565         | 9.0   | 12798 | 0.6835          | 0.6423   | 0.0609 | 0.3516         |
  | 0.5537        | 10.0  | 14220 | 0.6816          | 0.6516   | 0.1130 | 0.3823         |
  | 0.5446        | 11.0  | 15642 | 0.6907          | 0.6392   | 0.0427 | 0.3409         |
  | 0.5368        | 12.0  | 17064 | 0.6788          | 0.6476   | 0.0921 | 0.3699         |
  | 0.5305        | 13.0  | 18486 | 0.6729          | 0.6531   | 0.1216 | 0.3874         |
  | 0.525         | 14.0  | 19908 | 0.6704          | 0.6417   | 0.0575 | 0.3496         |
  | 0.5206        | 15.0  | 21330 | 0.6757          | 0.6467   | 0.0873 | 0.3670         |
  | 0.5165        | 16.0  | 22752 | 0.6805          | 0.6481   | 0.0940 | 0.3711         |
  | 0.513         | 17.0  | 24174 | 0.6760          | 0.6474   | 0.0901 | 0.3688         |
  | 0.5103        | 18.0  | 25596 | 0.6767          | 0.6505   | 0.1071 | 0.3788         |
  | 0.5074        | 19.0  | 27018 | 0.6798          | 0.6486   | 0.0964 | 0.3725         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  