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_96
    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: 0.6329210981944101
      - name: F1
        type: f1
        value: 0.006160851804727785
  ---
  
  <!-- 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_96
  
  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: 0.7423
  - Accuracy: 0.6329
  - F1: 0.0062
  - Combined Score: 0.3195
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.8963        | 1.0   | 1422  | 0.7832          | 0.6318   | 0.0    | 0.3159         |
  | 0.7734        | 2.0   | 2844  | 0.7741          | 0.6318   | 0.0    | 0.3159         |
  | 0.7598        | 3.0   | 4266  | 0.7727          | 0.6318   | 0.0    | 0.3159         |
  | 0.7474        | 4.0   | 5688  | 0.7675          | 0.6318   | 0.0    | 0.3159         |
  | 0.7366        | 5.0   | 7110  | 0.7626          | 0.6318   | 0.0    | 0.3159         |
  | 0.7272        | 6.0   | 8532  | 0.7568          | 0.6318   | 0.0    | 0.3159         |
  | 0.7177        | 7.0   | 9954  | 0.7539          | 0.6318   | 0.0    | 0.3159         |
  | 0.7084        | 8.0   | 11376 | 0.7500          | 0.6318   | 0.0    | 0.3159         |
  | 0.6998        | 9.0   | 12798 | 0.7543          | 0.6318   | 0.0    | 0.3159         |
  | 0.692         | 10.0  | 14220 | 0.7469          | 0.6318   | 0.0    | 0.3159         |
  | 0.6846        | 11.0  | 15642 | 0.7481          | 0.6318   | 0.0    | 0.3159         |
  | 0.6774        | 12.0  | 17064 | 0.7486          | 0.6318   | 0.0    | 0.3159         |
  | 0.6705        | 13.0  | 18486 | 0.7440          | 0.6318   | 0.0    | 0.3159         |
  | 0.6648        | 14.0  | 19908 | 0.7464          | 0.6318   | 0.0    | 0.3159         |
  | 0.659         | 15.0  | 21330 | 0.7430          | 0.6318   | 0.0    | 0.3159         |
  | 0.6531        | 16.0  | 22752 | 0.7423          | 0.6329   | 0.0062 | 0.3195         |
  | 0.6479        | 17.0  | 24174 | 0.7452          | 0.6321   | 0.0016 | 0.3169         |
  | 0.643         | 18.0  | 25596 | 0.7443          | 0.6354   | 0.0214 | 0.3284         |
  | 0.6387        | 19.0  | 27018 | 0.7431          | 0.6335   | 0.0092 | 0.3213         |
  | 0.6343        | 20.0  | 28440 | 0.7436          | 0.6370   | 0.0318 | 0.3344         |
  | 0.6297        | 21.0  | 29862 | 0.7444          | 0.6362   | 0.0266 | 0.3314         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  