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_pretrain_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: 0.663195646796933
      - name: F1
        type: f1
        value: 0.16465247530826327
  ---
  
  <!-- 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_pretrain_qqp
  
  This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5449
  - Accuracy: 0.6632
  - F1: 0.1647
  - Combined Score: 0.4139
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6004        | 1.0   | 1422  | 0.5643          | 0.6623   | 0.1630 | 0.4126         |
  | 0.5393        | 2.0   | 2844  | 0.5498          | 0.6538   | 0.1199 | 0.3869         |
  | 0.5157        | 3.0   | 4266  | 0.5449          | 0.6632   | 0.1647 | 0.4139         |
  | 0.5007        | 4.0   | 5688  | 0.5512          | 0.6848   | 0.2663 | 0.4755         |
  | 0.4914        | 5.0   | 7110  | 0.5501          | 0.6665   | 0.1817 | 0.4241         |
  | 0.4847        | 6.0   | 8532  | 0.5475          | 0.6816   | 0.2517 | 0.4667         |
  | 0.4803        | 7.0   | 9954  | 0.5478          | 0.6768   | 0.2301 | 0.4535         |
  | 0.4768        | 8.0   | 11376 | 0.5488          | 0.6839   | 0.2610 | 0.4724         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  