readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_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: 0.7784071234232006
      - name: F1
        type: f1
        value: 0.6885884111369878
  ---
  
  <!-- 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_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: 0.4700
  - Accuracy: 0.7784
  - F1: 0.6886
  - Combined Score: 0.7335
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5294        | 1.0   | 2843  | 0.5076          | 0.7512   | 0.6636 | 0.7074         |
  | 0.4791        | 2.0   | 5686  | 0.4889          | 0.7613   | 0.6369 | 0.6991         |
  | 0.4622        | 3.0   | 8529  | 0.4821          | 0.7657   | 0.6475 | 0.7066         |
  | 0.4463        | 4.0   | 11372 | 0.4831          | 0.7694   | 0.6730 | 0.7212         |
  | 0.4288        | 5.0   | 14215 | 0.4724          | 0.7752   | 0.6784 | 0.7268         |
  | 0.4129        | 6.0   | 17058 | 0.4806          | 0.7749   | 0.6893 | 0.7321         |
  | 0.3969        | 7.0   | 19901 | 0.4700          | 0.7784   | 0.6886 | 0.7335         |
  | 0.3813        | 8.0   | 22744 | 0.4802          | 0.7790   | 0.6962 | 0.7376         |
  | 0.3664        | 9.0   | 25587 | 0.4765          | 0.7805   | 0.6952 | 0.7378         |
  | 0.352         | 10.0  | 28430 | 0.4965          | 0.7768   | 0.7086 | 0.7427         |
  | 0.3381        | 11.0  | 31273 | 0.4895          | 0.7845   | 0.6960 | 0.7403         |
  | 0.3258        | 12.0  | 34116 | 0.5092          | 0.7844   | 0.7043 | 0.7444         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  