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
    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: 0.756987385604749
      - name: F1
        type: f1
        value: 0.604929832321364
  ---
  
  <!-- 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
  
  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: 0.8079
  - Accuracy: 0.7570
  - F1: 0.6049
  - Combined Score: 0.6810
  
  ## 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 |
  |:--------------------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 1.2837                     | 1.0   | 2843  | 1.2201          | 0.6318   | 0.0    | 0.3159         |
  | 1.076                      | 2.0   | 5686  | 0.8477          | 0.7443   | 0.5855 | 0.6649         |
  | 0.866                      | 3.0   | 8529  | 0.8217          | 0.7518   | 0.5924 | 0.6721         |
  | 0.8317                     | 4.0   | 11372 | 0.8136          | 0.7565   | 0.6243 | 0.6904         |
  | 0.8122                     | 5.0   | 14215 | 0.8126          | 0.7588   | 0.6352 | 0.6970         |
  | 0.799                      | 6.0   | 17058 | 0.8079          | 0.7570   | 0.6049 | 0.6810         |
  | 386581134871678353408.0000 | 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         |
  | 0.0                        | 11.0  | 31273 | 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
  