---
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
  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

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: 0.1256
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2964        | 1.0   | 1959  | 0.2026          | 0.9608   | 0.9718 | 0.9663         |
| 0.2307        | 2.0   | 3918  | 0.1943          | 0.9706   | 0.9789 | 0.9748         |
| 0.2221        | 3.0   | 5877  | 0.1874          | 0.9804   | 0.9858 | 0.9831         |
| 0.2163        | 4.0   | 7836  | 0.1703          | 0.9853   | 0.9894 | 0.9873         |
| 0.2115        | 5.0   | 9795  | 0.1805          | 0.9853   | 0.9894 | 0.9873         |
| 0.2071        | 6.0   | 11754 | 0.1682          | 0.9804   | 0.9859 | 0.9831         |
| 0.2036        | 7.0   | 13713 | 0.1583          | 0.9877   | 0.9911 | 0.9894         |
| 0.2007        | 8.0   | 15672 | 0.1628          | 0.9926   | 0.9947 | 0.9936         |
| 0.1985        | 9.0   | 17631 | 0.1548          | 0.9853   | 0.9894 | 0.9873         |
| 0.1965        | 10.0  | 19590 | 0.1583          | 0.9877   | 0.9911 | 0.9894         |
| 0.195         | 11.0  | 21549 | 0.1527          | 0.9902   | 0.9928 | 0.9915         |
| 0.1938        | 12.0  | 23508 | 0.1512          | 0.9902   | 0.9929 | 0.9915         |
| 0.1926        | 13.0  | 25467 | 0.1426          | 0.9951   | 0.9964 | 0.9958         |
| 0.1917        | 14.0  | 27426 | 0.1436          | 0.9951   | 0.9964 | 0.9958         |
| 0.191         | 15.0  | 29385 | 0.1503          | 0.9926   | 0.9946 | 0.9936         |
| 0.1901        | 16.0  | 31344 | 0.1461          | 0.9951   | 0.9964 | 0.9958         |
| 0.1894        | 17.0  | 33303 | 0.1498          | 0.9975   | 0.9982 | 0.9979         |
| 0.1888        | 18.0  | 35262 | 0.1402          | 0.9902   | 0.9929 | 0.9915         |
| 0.1882        | 19.0  | 37221 | 0.1420          | 0.9926   | 0.9946 | 0.9936         |
| 0.1876        | 20.0  | 39180 | 0.1346          | 0.9902   | 0.9929 | 0.9915         |
| 0.1871        | 21.0  | 41139 | 0.1396          | 0.9951   | 0.9964 | 0.9958         |
| 0.1867        | 22.0  | 43098 | 0.1443          | 0.9951   | 0.9964 | 0.9958         |
| 0.1862        | 23.0  | 45057 | 0.1346          | 0.9926   | 0.9947 | 0.9936         |
| 0.1857        | 24.0  | 47016 | 0.1361          | 0.9951   | 0.9964 | 0.9958         |
| 0.1854        | 25.0  | 48975 | 0.1318          | 0.9926   | 0.9947 | 0.9936         |
| 0.185         | 26.0  | 50934 | 0.1310          | 0.9902   | 0.9929 | 0.9915         |
| 0.1846        | 27.0  | 52893 | 0.1302          | 0.9926   | 0.9947 | 0.9936         |
| 0.1842        | 28.0  | 54852 | 0.1329          | 0.9951   | 0.9964 | 0.9958         |
| 0.1839        | 29.0  | 56811 | 0.1300          | 0.9902   | 0.9929 | 0.9915         |
| 0.1836        | 30.0  | 58770 | 0.1328          | 0.9902   | 0.9929 | 0.9915         |
| 0.1832        | 31.0  | 60729 | 0.1327          | 0.9902   | 0.9929 | 0.9915         |
| 0.1829        | 32.0  | 62688 | 0.1308          | 0.9902   | 0.9929 | 0.9915         |
| 0.1826        | 33.0  | 64647 | 0.1287          | 0.9902   | 0.9929 | 0.9915         |
| 0.1824        | 34.0  | 66606 | 0.1309          | 0.9926   | 0.9947 | 0.9936         |
| 0.1821        | 35.0  | 68565 | 0.1309          | 0.9926   | 0.9947 | 0.9936         |
| 0.1818        | 36.0  | 70524 | 0.1271          | 0.9902   | 0.9929 | 0.9915         |
| 0.1816        | 37.0  | 72483 | 0.1278          | 0.9877   | 0.9911 | 0.9894         |
| 0.1813        | 38.0  | 74442 | 0.1280          | 0.9902   | 0.9929 | 0.9915         |
| 0.1811        | 39.0  | 76401 | 0.1289          | 0.9902   | 0.9929 | 0.9915         |
| 0.1809        | 40.0  | 78360 | 0.1290          | 0.9877   | 0.9911 | 0.9894         |
| 0.1807        | 41.0  | 80319 | 0.1256          | 0.9877   | 0.9911 | 0.9894         |
| 0.1805        | 42.0  | 82278 | 0.1268          | 0.9926   | 0.9947 | 0.9936         |
| 0.1803        | 43.0  | 84237 | 0.1274          | 0.9926   | 0.9947 | 0.9936         |
| 0.1801        | 44.0  | 86196 | 0.1277          | 0.9926   | 0.9947 | 0.9936         |
| 0.1799        | 45.0  | 88155 | 0.1264          | 0.9926   | 0.9947 | 0.9936         |
| 0.1797        | 46.0  | 90114 | 0.1274          | 0.9902   | 0.9929 | 0.9915         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2