readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        config: qnli
        split: validation
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8984074684239429
  ---
  
  <!-- 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_logit_kd_pretrain_qnli
  
  This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2158
  - Accuracy: 0.8984
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.1418        | 1.0   | 819   | 1.0623          | 0.5054   |
  | 1.1397        | 2.0   | 1638  | 1.0617          | 0.5054   |
  | 1.1439        | 3.0   | 2457  | 1.0634          | 0.5054   |
  | 1.1397        | 4.0   | 3276  | 1.0635          | 0.5054   |
  | 1.14          | 5.0   | 4095  | 1.0643          | 0.5054   |
  | 1.1399        | 6.0   | 4914  | 1.0611          | 0.5054   |
  | 1.14          | 7.0   | 5733  | 1.0625          | 0.5054   |
  | 1.0013        | 8.0   | 6552  | 0.3801          | 0.8420   |
  | 0.3353        | 9.0   | 7371  | 0.2163          | 0.9030   |
  | 0.2165        | 10.0  | 8190  | 0.2158          | 0.8984   |
  | 0.1593        | 11.0  | 9009  | 0.2205          | 0.9057   |
  | 0.126         | 12.0  | 9828  | 0.2291          | 0.9077   |
  | 0.1049        | 13.0  | 10647 | 0.2323          | 0.9072   |
  | 0.0903        | 14.0  | 11466 | 0.2676          | 0.8984   |
  | 0.0819        | 15.0  | 12285 | 0.2377          | 0.9006   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  