readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_add_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: 0.7599802127133317
      - name: F1
        type: f1
        value: 0.6401928068223952
  ---
  
  <!-- 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_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: 0.5008
  - Accuracy: 0.7600
  - F1: 0.6402
  - Combined Score: 0.7001
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6505        | 1.0   | 2843  | 0.6498          | 0.6321   | 0.0012 | 0.3166         |
  | 0.6474        | 2.0   | 5686  | 0.6484          | 0.6321   | 0.0012 | 0.3166         |
  | 0.646         | 3.0   | 8529  | 0.6479          | 0.6322   | 0.0024 | 0.3173         |
  | 0.5481        | 4.0   | 11372 | 0.5140          | 0.7486   | 0.6247 | 0.6867         |
  | 0.4934        | 5.0   | 14215 | 0.5086          | 0.7529   | 0.6548 | 0.7039         |
  | 0.4794        | 6.0   | 17058 | 0.5044          | 0.7575   | 0.6527 | 0.7051         |
  | 0.4708        | 7.0   | 19901 | 0.5008          | 0.7600   | 0.6402 | 0.7001         |
  | 0.4652        | 8.0   | 22744 | 0.5010          | 0.7619   | 0.6384 | 0.7001         |
  | 0.4604        | 9.0   | 25587 | 0.5014          | 0.7614   | 0.6489 | 0.7052         |
  | 0.4562        | 10.0  | 28430 | 0.5057          | 0.7600   | 0.6617 | 0.7108         |
  | 0.452         | 11.0  | 31273 | 0.5102          | 0.7620   | 0.6364 | 0.6992         |
  | 0.4476        | 12.0  | 34116 | 0.5302          | 0.7622   | 0.6619 | 0.7121         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  