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_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: 0.6386099431115508
      - name: F1
        type: f1
        value: 0.039445138386693834
  ---
  
  <!-- 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_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: 0.6955
  - Accuracy: 0.6386
  - F1: 0.0394
  - Combined Score: 0.3390
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.8148        | 1.0   | 1422  | 0.7743          | 0.6318   | 0.0    | 0.3159         |
  | 0.7531        | 2.0   | 2844  | 0.7520          | 0.6318   | 0.0    | 0.3159         |
  | 0.7248        | 3.0   | 4266  | 0.7365          | 0.6318   | 0.0    | 0.3159         |
  | 0.6964        | 4.0   | 5688  | 0.7183          | 0.6444   | 0.0751 | 0.3598         |
  | 0.676         | 5.0   | 7110  | 0.7078          | 0.6346   | 0.0165 | 0.3256         |
  | 0.6579        | 6.0   | 8532  | 0.7030          | 0.6359   | 0.0241 | 0.3300         |
  | 0.6427        | 7.0   | 9954  | 0.7007          | 0.6332   | 0.0078 | 0.3205         |
  | 0.6291        | 8.0   | 11376 | 0.7044          | 0.6389   | 0.0415 | 0.3402         |
  | 0.6173        | 9.0   | 12798 | 0.7002          | 0.6407   | 0.0522 | 0.3465         |
  | 0.6061        | 10.0  | 14220 | 0.6971          | 0.6406   | 0.0526 | 0.3466         |
  | 0.5967        | 11.0  | 15642 | 0.6955          | 0.6386   | 0.0394 | 0.3390         |
  | 0.5871        | 12.0  | 17064 | 0.7006          | 0.6470   | 0.0874 | 0.3672         |
  | 0.5792        | 13.0  | 18486 | 0.6957          | 0.6426   | 0.0625 | 0.3526         |
  | 0.5722        | 14.0  | 19908 | 0.7026          | 0.6390   | 0.0415 | 0.3402         |
  | 0.5658        | 15.0  | 21330 | 0.6983          | 0.6432   | 0.0658 | 0.3545         |
  | 0.5597        | 16.0  | 22752 | 0.7013          | 0.6405   | 0.0503 | 0.3454         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  