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_384
    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: 0.6454365570121197
      - name: F1
        type: f1
        value: 0.07878671036565774
  ---
  
  <!-- 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_384
  
  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: 0.6771
  - Accuracy: 0.6454
  - F1: 0.0788
  - Combined Score: 0.3621
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.7984        | 1.0   | 1422  | 0.7600          | 0.6318   | 0.0    | 0.3159         |
  | 0.7388        | 2.0   | 2844  | 0.7348          | 0.6318   | 0.0    | 0.3159         |
  | 0.7037        | 3.0   | 4266  | 0.7082          | 0.6329   | 0.0056 | 0.3192         |
  | 0.6717        | 4.0   | 5688  | 0.7014          | 0.6474   | 0.0908 | 0.3691         |
  | 0.6462        | 5.0   | 7110  | 0.6841          | 0.6377   | 0.0339 | 0.3358         |
  | 0.6259        | 6.0   | 8532  | 0.6795          | 0.6382   | 0.0364 | 0.3373         |
  | 0.6092        | 7.0   | 9954  | 0.6782          | 0.6408   | 0.0513 | 0.3461         |
  | 0.5941        | 8.0   | 11376 | 0.6771          | 0.6454   | 0.0788 | 0.3621         |
  | 0.5812        | 9.0   | 12798 | 0.6841          | 0.6492   | 0.0991 | 0.3741         |
  | 0.5703        | 10.0  | 14220 | 0.6774          | 0.6452   | 0.0776 | 0.3614         |
  | 0.5604        | 11.0  | 15642 | 0.6791          | 0.6464   | 0.0831 | 0.3647         |
  | 0.5523        | 12.0  | 17064 | 0.6817          | 0.6520   | 0.1143 | 0.3831         |
  | 0.5448        | 13.0  | 18486 | 0.6774          | 0.6477   | 0.0905 | 0.3691         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  