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_128
    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: 0.7871877318822657
      - name: F1
        type: f1
        value: 0.7061676115019466
  ---
  
  <!-- 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_128
  
  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: 0.6884
  - Accuracy: 0.7872
  - F1: 0.7062
  - Combined Score: 0.7467
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.9518        | 1.0   | 2843  | 0.8352          | 0.7536   | 0.6530 | 0.7033         |
  | 0.8249        | 2.0   | 5686  | 0.7766          | 0.7607   | 0.6219 | 0.6913         |
  | 0.7847        | 3.0   | 8529  | 0.7625          | 0.7648   | 0.6402 | 0.7025         |
  | 0.7498        | 4.0   | 11372 | 0.7551          | 0.7638   | 0.6197 | 0.6917         |
  | 0.7137        | 5.0   | 14215 | 0.7387          | 0.7691   | 0.6545 | 0.7118         |
  | 0.6762        | 6.0   | 17058 | 0.7165          | 0.7753   | 0.6720 | 0.7237         |
  | 0.6373        | 7.0   | 19901 | 0.7042          | 0.7783   | 0.6765 | 0.7274         |
  | 0.6045        | 8.0   | 22744 | 0.7075          | 0.7799   | 0.6902 | 0.7350         |
  | 0.5729        | 9.0   | 25587 | 0.7233          | 0.7792   | 0.6639 | 0.7215         |
  | 0.545         | 10.0  | 28430 | 0.7088          | 0.7805   | 0.7180 | 0.7493         |
  | 0.5183        | 11.0  | 31273 | 0.6884          | 0.7872   | 0.7062 | 0.7467         |
  | 0.4948        | 12.0  | 34116 | 0.7064          | 0.7869   | 0.7076 | 0.7472         |
  | 0.4724        | 13.0  | 36959 | 0.7053          | 0.7884   | 0.7120 | 0.7502         |
  | 0.4514        | 14.0  | 39802 | 0.7314          | 0.7903   | 0.7024 | 0.7464         |
  | 0.4321        | 15.0  | 42645 | 0.7112          | 0.7891   | 0.7228 | 0.7560         |
  | 0.4152        | 16.0  | 45488 | 0.7410          | 0.7909   | 0.7211 | 0.7560         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  