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_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: 0.7595597328716299
      - name: F1
        type: f1
        value: 0.6364486330827631
  ---
  
  <!-- 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_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: 0.8027
  - Accuracy: 0.7596
  - F1: 0.6364
  - Combined Score: 0.6980
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 1.2838        | 1.0   | 2843  | 1.2200          | 0.6318   | 0.0    | 0.3159         |
  | 1.0184        | 2.0   | 5686  | 0.8422          | 0.7473   | 0.5924 | 0.6698         |
  | 0.8633        | 3.0   | 8529  | 0.8232          | 0.7520   | 0.5963 | 0.6742         |
  | 0.834         | 4.0   | 11372 | 0.8193          | 0.7563   | 0.6271 | 0.6917         |
  | 0.812         | 5.0   | 14215 | 0.8027          | 0.7596   | 0.6364 | 0.6980         |
  | 0.7871        | 6.0   | 17058 | nan             | 0.6318   | 0.0    | 0.3159         |
  | 0.0           | 7.0   | 19901 | nan             | 0.6318   | 0.0    | 0.3159         |
  | 0.0           | 8.0   | 22744 | nan             | 0.6318   | 0.0    | 0.3159         |
  | 0.0           | 9.0   | 25587 | nan             | 0.6318   | 0.0    | 0.3159         |
  | 0.0           | 10.0  | 28430 | nan             | 0.6318   | 0.0    | 0.3159         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  