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_pretrain_mrpc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8578431372549019
      - name: F1
        type: f1
        value: 0.8993055555555555
  ---
  
  <!-- 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_pretrain_mrpc
  
  This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2291
  - Accuracy: 0.8578
  - F1: 0.8993
  - Combined Score: 0.8786
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.536         | 1.0   | 29   | 0.4134          | 0.7279   | 0.8284 | 0.7782         |
  | 0.3419        | 2.0   | 58   | 0.3005          | 0.8284   | 0.8801 | 0.8543         |
  | 0.2413        | 3.0   | 87   | 0.2707          | 0.8235   | 0.8780 | 0.8507         |
  | 0.1852        | 4.0   | 116  | 0.3247          | 0.8284   | 0.8837 | 0.8561         |
  | 0.1524        | 5.0   | 145  | 0.2856          | 0.8431   | 0.8900 | 0.8666         |
  | 0.1297        | 6.0   | 174  | 0.2999          | 0.8456   | 0.8948 | 0.8702         |
  | 0.1219        | 7.0   | 203  | 0.2797          | 0.8529   | 0.8986 | 0.8758         |
  | 0.1141        | 8.0   | 232  | 0.2462          | 0.8603   | 0.9005 | 0.8804         |
  | 0.1127        | 9.0   | 261  | 0.2557          | 0.8578   | 0.8982 | 0.8780         |
  | 0.1091        | 10.0  | 290  | 0.2853          | 0.8480   | 0.8967 | 0.8724         |
  | 0.1007        | 11.0  | 319  | 0.2472          | 0.8554   | 0.8981 | 0.8767         |
  | 0.0979        | 12.0  | 348  | 0.2431          | 0.8505   | 0.8950 | 0.8727         |
  | 0.0954        | 13.0  | 377  | 0.2456          | 0.8578   | 0.9007 | 0.8793         |
  | 0.0946        | 14.0  | 406  | 0.2526          | 0.8578   | 0.9017 | 0.8798         |
  | 0.0946        | 15.0  | 435  | 0.2291          | 0.8578   | 0.8993 | 0.8786         |
  | 0.0938        | 16.0  | 464  | 0.2452          | 0.8603   | 0.9029 | 0.8816         |
  | 0.0919        | 17.0  | 493  | 0.2365          | 0.8652   | 0.9050 | 0.8851         |
  | 0.0916        | 18.0  | 522  | 0.2363          | 0.8652   | 0.9060 | 0.8856         |
  | 0.0915        | 19.0  | 551  | 0.2432          | 0.8652   | 0.9063 | 0.8857         |
  | 0.0905        | 20.0  | 580  | 0.2297          | 0.8652   | 0.9057 | 0.8854         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  