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
    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: 0.8081127875340094
      - name: F1
        type: f1
        value: 0.7357269382749694
  ---
  
  <!-- 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
  
  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: 0.6021
  - Accuracy: 0.8081
  - F1: 0.7357
  - Combined Score: 0.7719
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.9285        | 1.0   | 2843  | 0.8489          | 0.7517   | 0.6660 | 0.7089         |
  | 0.7919        | 2.0   | 5686  | 0.7104          | 0.7735   | 0.6483 | 0.7109         |
  | 0.6916        | 3.0   | 8529  | 0.6615          | 0.7900   | 0.6994 | 0.7447         |
  | 0.6382        | 4.0   | 11372 | 0.6606          | 0.7899   | 0.6842 | 0.7370         |
  | 0.6003        | 5.0   | 14215 | 0.6277          | 0.7988   | 0.7181 | 0.7585         |
  | 0.5696        | 6.0   | 17058 | 0.6174          | 0.7980   | 0.7058 | 0.7519         |
  | 0.5434        | 7.0   | 19901 | 0.6062          | 0.8036   | 0.7266 | 0.7651         |
  | 0.5186        | 8.0   | 22744 | 0.6182          | 0.7995   | 0.7232 | 0.7614         |
  | 0.4968        | 9.0   | 25587 | 0.6087          | 0.8052   | 0.7257 | 0.7654         |
  | 0.4758        | 10.0  | 28430 | 0.6035          | 0.8073   | 0.7359 | 0.7716         |
  | 0.456         | 11.0  | 31273 | 0.6021          | 0.8081   | 0.7357 | 0.7719         |
  | 0.4361        | 12.0  | 34116 | 0.6137          | 0.8070   | 0.7366 | 0.7718         |
  | 0.4186        | 13.0  | 36959 | 0.6282          | 0.8076   | 0.7416 | 0.7746         |
  | 0.4009        | 14.0  | 39802 | 0.6183          | 0.8093   | 0.7445 | 0.7769         |
  | 0.3846        | 15.0  | 42645 | 0.6196          | 0.8057   | 0.7483 | 0.7770         |
  | 0.3699        | 16.0  | 45488 | 0.6381          | 0.8122   | 0.7426 | 0.7774         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  