readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_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: 0.8006678209250556
      - name: F1
        type: f1
        value: 0.7300800482298958
  ---
  
  <!-- 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_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: 0.4287
  - Accuracy: 0.8007
  - F1: 0.7301
  - Combined Score: 0.7654
  
  ## 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5237        | 1.0   | 2843  | 0.5087          | 0.7472   | 0.6697 | 0.7085         |
  | 0.4614        | 2.0   | 5686  | 0.4697          | 0.7754   | 0.6746 | 0.7250         |
  | 0.4287        | 3.0   | 8529  | 0.4508          | 0.7853   | 0.6893 | 0.7373         |
  | 0.4089        | 4.0   | 11372 | 0.4493          | 0.7925   | 0.7151 | 0.7538         |
  | 0.3904        | 5.0   | 14215 | 0.4361          | 0.7984   | 0.7222 | 0.7603         |
  | 0.3752        | 6.0   | 17058 | 0.4332          | 0.8023   | 0.7215 | 0.7619         |
  | 0.3592        | 7.0   | 19901 | 0.4287          | 0.8007   | 0.7301 | 0.7654         |
  | 0.3458        | 8.0   | 22744 | 0.4337          | 0.8005   | 0.7324 | 0.7664         |
  | 0.3326        | 9.0   | 25587 | 0.4340          | 0.8006   | 0.7362 | 0.7684         |
  | 0.3201        | 10.0  | 28430 | 0.4464          | 0.8028   | 0.7417 | 0.7722         |
  | 0.3092        | 11.0  | 31273 | 0.4615          | 0.8037   | 0.7196 | 0.7617         |
  | 0.2984        | 12.0  | 34116 | 0.4763          | 0.8047   | 0.7326 | 0.7687         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  