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_sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        config: sst2
        split: validation
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.926605504587156
  ---
  
  <!-- 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_sst2
  
  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 SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2364
  - Accuracy: 0.9266
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.4176        | 1.0   | 527   | 0.2978          | 0.9197   |
  | 0.1807        | 2.0   | 1054  | 0.2951          | 0.9174   |
  | 0.1163        | 3.0   | 1581  | 0.2749          | 0.9186   |
  | 0.0862        | 4.0   | 2108  | 0.2988          | 0.9083   |
  | 0.0695        | 5.0   | 2635  | 0.2760          | 0.9174   |
  | 0.0598        | 6.0   | 3162  | 0.2695          | 0.9151   |
  | 0.0525        | 7.0   | 3689  | 0.2723          | 0.9255   |
  | 0.0464        | 8.0   | 4216  | 0.2430          | 0.9243   |
  | 0.0422        | 9.0   | 4743  | 0.2814          | 0.9243   |
  | 0.0395        | 10.0  | 5270  | 0.2464          | 0.9163   |
  | 0.0357        | 11.0  | 5797  | 0.2390          | 0.9197   |
  | 0.0341        | 12.0  | 6324  | 0.2713          | 0.9197   |
  | 0.0328        | 13.0  | 6851  | 0.2685          | 0.9220   |
  | 0.0315        | 14.0  | 7378  | 0.2585          | 0.9186   |
  | 0.0296        | 15.0  | 7905  | 0.2367          | 0.9220   |
  | 0.0283        | 16.0  | 8432  | 0.2560          | 0.9186   |
  | 0.0277        | 17.0  | 8959  | 0.2635          | 0.9174   |
  | 0.0269        | 18.0  | 9486  | 0.2364          | 0.9266   |
  | 0.026         | 19.0  | 10013 | 0.2749          | 0.9209   |
  | 0.0252        | 20.0  | 10540 | 0.2507          | 0.9174   |
  | 0.0248        | 21.0  | 11067 | 0.2769          | 0.9163   |
  | 0.0248        | 22.0  | 11594 | 0.2543          | 0.9220   |
  | 0.024         | 23.0  | 12121 | 0.2677          | 0.9209   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  