readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_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: 0.6400692555033392
      - name: F1
        type: f1
        value: 0.04839131572063824
  ---
  
  <!-- 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_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: 0.7047
  - Accuracy: 0.6401
  - F1: 0.0484
  - Combined Score: 0.3442
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.824         | 1.0   | 1422  | 0.7761          | 0.6318   | 0.0    | 0.3159         |
  | 0.7581        | 2.0   | 2844  | 0.7544          | 0.6318   | 0.0    | 0.3159         |
  | 0.7333        | 3.0   | 4266  | 0.7480          | 0.6318   | 0.0    | 0.3159         |
  | 0.713         | 4.0   | 5688  | 0.7358          | 0.6318   | 0.0    | 0.3159         |
  | 0.695         | 5.0   | 7110  | 0.7279          | 0.6365   | 0.0280 | 0.3323         |
  | 0.6783        | 6.0   | 8532  | 0.7193          | 0.6370   | 0.0313 | 0.3341         |
  | 0.6642        | 7.0   | 9954  | 0.7148          | 0.6332   | 0.0083 | 0.3208         |
  | 0.6509        | 8.0   | 11376 | 0.7138          | 0.6409   | 0.0536 | 0.3472         |
  | 0.639         | 9.0   | 12798 | 0.7084          | 0.6385   | 0.0398 | 0.3391         |
  | 0.6287        | 10.0  | 14220 | 0.7117          | 0.6418   | 0.0591 | 0.3504         |
  | 0.6195        | 11.0  | 15642 | 0.7064          | 0.6379   | 0.0363 | 0.3371         |
  | 0.6101        | 12.0  | 17064 | 0.7079          | 0.6444   | 0.0725 | 0.3584         |
  | 0.6023        | 13.0  | 18486 | 0.7059          | 0.6495   | 0.1028 | 0.3762         |
  | 0.5948        | 14.0  | 19908 | 0.7103          | 0.6418   | 0.0577 | 0.3497         |
  | 0.589         | 15.0  | 21330 | 0.7077          | 0.6415   | 0.0567 | 0.3491         |
  | 0.5827        | 16.0  | 22752 | 0.7047          | 0.6401   | 0.0484 | 0.3442         |
  | 0.5769        | 17.0  | 24174 | 0.7051          | 0.6409   | 0.0525 | 0.3467         |
  | 0.572         | 18.0  | 25596 | 0.7090          | 0.6492   | 0.1017 | 0.3754         |
  | 0.5673        | 19.0  | 27018 | 0.7078          | 0.6398   | 0.0467 | 0.3433         |
  | 0.563         | 20.0  | 28440 | 0.7065          | 0.6427   | 0.0630 | 0.3529         |
  | 0.5591        | 21.0  | 29862 | 0.7084          | 0.6464   | 0.0839 | 0.3651         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  