readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_add_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: 0.7763047242146921
      - name: F1
        type: f1
        value: 0.6592313489073096
  ---
  
  <!-- 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_add_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: 0.7254
  - Accuracy: 0.7763
  - F1: 0.6592
  - Combined Score: 0.7178
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 1.1514        | 1.0   | 2843  | 0.9015          | 0.7377   | 0.6339 | 0.6858         |
  | 0.8815        | 2.0   | 5686  | 0.8232          | 0.7538   | 0.6095 | 0.6817         |
  | 0.8373        | 3.0   | 8529  | 0.8122          | 0.7591   | 0.6325 | 0.6958         |
  | 0.8086        | 4.0   | 11372 | 0.8008          | 0.7562   | 0.6018 | 0.6790         |
  | 0.7833        | 5.0   | 14215 | 0.7891          | 0.7638   | 0.6390 | 0.7014         |
  | 0.7568        | 6.0   | 17058 | 0.7867          | 0.7629   | 0.6188 | 0.6908         |
  | 0.7227        | 7.0   | 19901 | 0.7667          | 0.7717   | 0.6623 | 0.7170         |
  | 0.6868        | 8.0   | 22744 | 0.7315          | 0.7760   | 0.6597 | 0.7179         |
  | 0.6563        | 9.0   | 25587 | 0.7254          | 0.7763   | 0.6592 | 0.7178         |
  | 0.6325        | 10.0  | 28430 | 0.7326          | 0.7768   | 0.6775 | 0.7272         |
  | 0.6116        | 11.0  | 31273 | 0.7327          | 0.7795   | 0.6748 | 0.7272         |
  | 0.59          | 12.0  | 34116 | 0.7386          | 0.7813   | 0.6779 | 0.7296         |
  | 0.5703        | 13.0  | 36959 | 0.7522          | 0.7806   | 0.6776 | 0.7291         |
  | 0.5516        | 14.0  | 39802 | 0.7574          | 0.7776   | 0.7031 | 0.7403         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  