readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_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: 0.7976007914914668
      - name: F1
        type: f1
        value: 0.7297109826589595
  ---
  
  <!-- 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_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: 0.4349
  - Accuracy: 0.7976
  - F1: 0.7297
  - Combined Score: 0.7637
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.526         | 1.0   | 2843  | 0.5088          | 0.7492   | 0.6674 | 0.7083         |
  | 0.4762        | 2.0   | 5686  | 0.4782          | 0.7695   | 0.6583 | 0.7139         |
  | 0.4438        | 3.0   | 8529  | 0.4532          | 0.7847   | 0.6829 | 0.7338         |
  | 0.4161        | 4.0   | 11372 | 0.4602          | 0.7869   | 0.7135 | 0.7502         |
  | 0.3968        | 5.0   | 14215 | 0.4395          | 0.7955   | 0.7212 | 0.7583         |
  | 0.3815        | 6.0   | 17058 | 0.4392          | 0.7985   | 0.7190 | 0.7587         |
  | 0.3659        | 7.0   | 19901 | 0.4349          | 0.7976   | 0.7297 | 0.7637         |
  | 0.352         | 8.0   | 22744 | 0.4419          | 0.8005   | 0.7300 | 0.7652         |
  | 0.3399        | 9.0   | 25587 | 0.4454          | 0.7998   | 0.7317 | 0.7658         |
  | 0.327         | 10.0  | 28430 | 0.4614          | 0.7995   | 0.7359 | 0.7677         |
  | 0.3157        | 11.0  | 31273 | 0.4733          | 0.8000   | 0.7246 | 0.7623         |
  | 0.3041        | 12.0  | 34116 | 0.4738          | 0.8041   | 0.7283 | 0.7662         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  