readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_logit_kd_mnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MNLI
        type: glue
        config: mnli
        split: validation_matched
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6172701383238405
  ---
  
  <!-- 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_mnli
  
  This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1966
  - Accuracy: 0.6173
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.6232        | 1.0   | 3068  | 1.3870          | 0.5505   |
  | 1.4341        | 2.0   | 6136  | 1.3186          | 0.5834   |
  | 1.3724        | 3.0   | 9204  | 1.2819          | 0.5943   |
  | 1.3249        | 4.0   | 12272 | 1.2702          | 0.5982   |
  | 1.2788        | 5.0   | 15340 | 1.2359          | 0.6031   |
  | 1.2302        | 6.0   | 18408 | 1.2008          | 0.6193   |
  | 1.1842        | 7.0   | 21476 | 1.1991          | 0.6222   |
  | 1.1441        | 8.0   | 24544 | 1.1839          | 0.6202   |
  | 1.1057        | 9.0   | 27612 | 1.1861          | 0.6244   |
  | 1.0715        | 10.0  | 30680 | 1.1755          | 0.6250   |
  | 1.0386        | 11.0  | 33748 | 1.1972          | 0.6313   |
  | 1.0066        | 12.0  | 36816 | 1.2149          | 0.6276   |
  | 0.9767        | 13.0  | 39884 | 1.2187          | 0.6193   |
  | 0.9482        | 14.0  | 42952 | 1.2004          | 0.6226   |
  | 0.921         | 15.0  | 46020 | 1.2093          | 0.6194   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  