readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256
    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: 0.794855305466238
      - name: F1
        type: f1
        value: 0.7224044447419508
  ---
  
  <!-- 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. -->
  
  # mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256
  
  This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6619
  - Accuracy: 0.7949
  - F1: 0.7224
  - Combined Score: 0.7586
  
  ## 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: 128
  - eval_batch_size: 128
  - 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
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.9454        | 1.0   | 2843  | 0.8257          | 0.7556   | 0.6563 | 0.7059         |
  | 0.8165        | 2.0   | 5686  | 0.7682          | 0.7590   | 0.6049 | 0.6820         |
  | 0.7741        | 3.0   | 8529  | 0.7514          | 0.7638   | 0.6203 | 0.6920         |
  | 0.7325        | 4.0   | 11372 | 0.7354          | 0.7675   | 0.6288 | 0.6981         |
  | 0.6849        | 5.0   | 14215 | 0.7063          | 0.7785   | 0.6818 | 0.7302         |
  | 0.6399        | 6.0   | 17058 | 0.6906          | 0.7828   | 0.6876 | 0.7352         |
  | 0.6005        | 7.0   | 19901 | 0.6771          | 0.7868   | 0.6993 | 0.7430         |
  | 0.5666        | 8.0   | 22744 | 0.6809          | 0.7897   | 0.7138 | 0.7517         |
  | 0.5365        | 9.0   | 25587 | 0.6807          | 0.7886   | 0.6921 | 0.7403         |
  | 0.5097        | 10.0  | 28430 | 0.6827          | 0.7873   | 0.7260 | 0.7566         |
  | 0.4856        | 11.0  | 31273 | 0.6619          | 0.7949   | 0.7224 | 0.7586         |
  | 0.4653        | 12.0  | 34116 | 0.7002          | 0.7948   | 0.7197 | 0.7572         |
  | 0.4438        | 13.0  | 36959 | 0.6900          | 0.7965   | 0.7203 | 0.7584         |
  | 0.4255        | 14.0  | 39802 | 0.6847          | 0.7981   | 0.7284 | 0.7632         |
  | 0.4072        | 15.0  | 42645 | 0.6893          | 0.7917   | 0.7336 | 0.7627         |
  | 0.391         | 16.0  | 45488 | 0.7136          | 0.7957   | 0.7300 | 0.7629         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  