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_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: 0.6541924313628493
      - name: F1
        type: f1
        value: 0.12196194184512969
  ---
  
  <!-- 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_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: 0.6543
  - Accuracy: 0.6542
  - F1: 0.1220
  - Combined Score: 0.3881
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.8184        | 1.0   | 1422  | 0.7776          | 0.6318   | 0.0    | 0.3159         |
  | 0.7189        | 2.0   | 2844  | 0.6932          | 0.6333   | 0.0083 | 0.3208         |
  | 0.6368        | 3.0   | 4266  | 0.6602          | 0.6326   | 0.0044 | 0.3185         |
  | 0.5938        | 4.0   | 5688  | 0.6756          | 0.6516   | 0.1109 | 0.3812         |
  | 0.5663        | 5.0   | 7110  | 0.6744          | 0.6398   | 0.0459 | 0.3428         |
  | 0.5478        | 6.0   | 8532  | 0.6685          | 0.6479   | 0.0888 | 0.3684         |
  | 0.5345        | 7.0   | 9954  | 0.6543          | 0.6542   | 0.1220 | 0.3881         |
  | 0.5241        | 8.0   | 11376 | 0.6601          | 0.6469   | 0.0848 | 0.3659         |
  | 0.5161        | 9.0   | 12798 | 0.6774          | 0.6590   | 0.1482 | 0.4036         |
  | 0.5099        | 10.0  | 14220 | 0.6590          | 0.6610   | 0.1594 | 0.4102         |
  | 0.5048        | 11.0  | 15642 | 0.6704          | 0.6529   | 0.1166 | 0.3847         |
  | 0.5001        | 12.0  | 17064 | 0.6791          | 0.6562   | 0.1347 | 0.3955         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  