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
    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: 0.6472916151372743
      - name: F1
        type: f1
        value: 0.08800204655922231
  ---
  
  <!-- 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
  
  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: 0.6308
  - Accuracy: 0.6473
  - F1: 0.0880
  - Combined Score: 0.3676
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.7821        | 1.0   | 1422  | 0.7485          | 0.6318   | 0.0    | 0.3159         |
  | 0.7105        | 2.0   | 2844  | 0.7038          | 0.6364   | 0.0261 | 0.3312         |
  | 0.6654        | 3.0   | 4266  | 0.6862          | 0.6351   | 0.0188 | 0.3269         |
  | 0.6284        | 4.0   | 5688  | 0.6610          | 0.6453   | 0.0779 | 0.3616         |
  | 0.5969        | 5.0   | 7110  | 0.6479          | 0.6416   | 0.0554 | 0.3485         |
  | 0.5712        | 6.0   | 8532  | 0.6457          | 0.6404   | 0.0497 | 0.3450         |
  | 0.5513        | 7.0   | 9954  | 0.6308          | 0.6473   | 0.0880 | 0.3676         |
  | 0.5349        | 8.0   | 11376 | 0.6351          | 0.6503   | 0.1037 | 0.3770         |
  | 0.5222        | 9.0   | 12798 | 0.6383          | 0.6719   | 0.2134 | 0.4427         |
  | 0.5124        | 10.0  | 14220 | 0.6392          | 0.6685   | 0.1991 | 0.4338         |
  | 0.5044        | 11.0  | 15642 | 0.6379          | 0.6615   | 0.1631 | 0.4123         |
  | 0.4978        | 12.0  | 17064 | 0.6363          | 0.6637   | 0.1750 | 0.4194         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  