readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_128
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9877450980392157
      - name: F1
        type: f1
        value: 0.9911190053285969
  ---
  
  <!-- 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_data_aug_mrpc_128
  
  This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1262
  - Accuracy: 0.9877
  - F1: 0.9911
  - Combined Score: 0.9894
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.3021        | 1.0   | 1959  | 0.2407          | 0.9510   | 0.9636 | 0.9573         |
  | 0.2326        | 2.0   | 3918  | 0.2009          | 0.9779   | 0.9841 | 0.9810         |
  | 0.224         | 3.0   | 5877  | 0.1790          | 0.9730   | 0.9807 | 0.9769         |
  | 0.219         | 4.0   | 7836  | 0.1789          | 0.9804   | 0.9859 | 0.9831         |
  | 0.2153        | 5.0   | 9795  | 0.1804          | 0.9804   | 0.9859 | 0.9831         |
  | 0.2121        | 6.0   | 11754 | 0.1754          | 0.9755   | 0.9824 | 0.9789         |
  | 0.2088        | 7.0   | 13713 | 0.1661          | 0.9804   | 0.9859 | 0.9831         |
  | 0.2056        | 8.0   | 15672 | 0.1654          | 0.9779   | 0.9841 | 0.9810         |
  | 0.2031        | 9.0   | 17631 | 0.1714          | 0.9828   | 0.9876 | 0.9852         |
  | 0.2012        | 10.0  | 19590 | 0.1617          | 0.9828   | 0.9876 | 0.9852         |
  | 0.1993        | 11.0  | 21549 | 0.1610          | 0.9828   | 0.9876 | 0.9852         |
  | 0.1978        | 12.0  | 23508 | 0.1507          | 0.9853   | 0.9894 | 0.9873         |
  | 0.1964        | 13.0  | 25467 | 0.1496          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1953        | 14.0  | 27426 | 0.1569          | 0.9828   | 0.9876 | 0.9852         |
  | 0.1943        | 15.0  | 29385 | 0.1524          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1934        | 16.0  | 31344 | 0.1492          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1926        | 17.0  | 33303 | 0.1480          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1918        | 18.0  | 35262 | 0.1416          | 0.9828   | 0.9876 | 0.9852         |
  | 0.1912        | 19.0  | 37221 | 0.1420          | 0.9853   | 0.9894 | 0.9873         |
  | 0.1905        | 20.0  | 39180 | 0.1396          | 0.9853   | 0.9894 | 0.9873         |
  | 0.1899        | 21.0  | 41139 | 0.1458          | 0.9853   | 0.9894 | 0.9873         |
  | 0.1893        | 22.0  | 43098 | 0.1484          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1888        | 23.0  | 45057 | 0.1407          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1883        | 24.0  | 47016 | 0.1372          | 0.9804   | 0.9858 | 0.9831         |
  | 0.1878        | 25.0  | 48975 | 0.1354          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1873        | 26.0  | 50934 | 0.1368          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1869        | 27.0  | 52893 | 0.1378          | 0.9926   | 0.9947 | 0.9936         |
  | 0.1865        | 28.0  | 54852 | 0.1381          | 0.9853   | 0.9894 | 0.9873         |
  | 0.1861        | 29.0  | 56811 | 0.1329          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1857        | 30.0  | 58770 | 0.1342          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1854        | 31.0  | 60729 | 0.1346          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1849        | 32.0  | 62688 | 0.1323          | 0.9902   | 0.9929 | 0.9915         |
  | 0.1846        | 33.0  | 64647 | 0.1317          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1843        | 34.0  | 66606 | 0.1318          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1839        | 35.0  | 68565 | 0.1311          | 0.9926   | 0.9947 | 0.9936         |
  | 0.1837        | 36.0  | 70524 | 0.1291          | 0.9877   | 0.9911 | 0.9894         |
  | 0.1834        | 37.0  | 72483 | 0.1313          | 0.9853   | 0.9893 | 0.9873         |
  |