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
    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: 0.6424931981202078
      - name: F1
        type: f1
        value: 0.06008583690987124
  ---
  
  <!-- 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
  
  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: 0.6623
  - Accuracy: 0.6425
  - F1: 0.0601
  - Combined Score: 0.3513
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.7968        | 1.0   | 1422  | 0.7159          | 0.6323   | 0.0030 | 0.3176         |
  | 0.6542        | 2.0   | 2844  | 0.6925          | 0.6338   | 0.0115 | 0.3226         |
  | 0.5893        | 3.0   | 4266  | 0.6695          | 0.6348   | 0.0172 | 0.3260         |
  | 0.5538        | 4.0   | 5688  | 0.7068          | 0.6386   | 0.0393 | 0.3390         |
  | 0.5323        | 5.0   | 7110  | 0.6670          | 0.6500   | 0.1014 | 0.3757         |
  | 0.5181        | 6.0   | 8532  | 0.6738          | 0.6420   | 0.0573 | 0.3497         |
  | 0.5082        | 7.0   | 9954  | 0.6623          | 0.6425   | 0.0601 | 0.3513         |
  | 0.5012        | 8.0   | 11376 | 0.6995          | 0.6412   | 0.0536 | 0.3474         |
  | 0.4957        | 9.0   | 12798 | 0.6836          | 0.6472   | 0.0858 | 0.3665         |
  | 0.4911        | 10.0  | 14220 | 0.6778          | 0.6484   | 0.0922 | 0.3703         |
  | 0.4874        | 11.0  | 15642 | 0.7183          | 0.6415   | 0.0550 | 0.3483         |
  | 0.484         | 12.0  | 17064 | 0.6730          | 0.6451   | 0.0744 | 0.3598         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  