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mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_256
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.1267
- Accuracy: 0.9926
- F1: 0.9947
- Combined Score: 0.9936
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 |
|---|---|---|---|---|---|---|
| 0.3017 | 1.0 | 1959 | 0.2241 | 0.9608 | 0.9713 | 0.9661 |
| 0.233 | 2.0 | 3918 | 0.2357 | 0.9828 | 0.9876 | 0.9852 |
| 0.2241 | 3.0 | 5877 | 0.1908 | 0.9706 | 0.9786 | 0.9746 |
| 0.2189 | 4.0 | 7836 | 0.1863 | 0.9755 | 0.9824 | 0.9789 |
| 0.2149 | 5.0 | 9795 | 0.1868 | 0.9804 | 0.9858 | 0.9831 |
| 0.211 | 6.0 | 11754 | 0.1735 | 0.9804 | 0.9859 | 0.9831 |
| 0.2073 | 7.0 | 13713 | 0.1875 | 0.9828 | 0.9876 | 0.9852 |
| 0.204 | 8.0 | 15672 | 0.1690 | 0.9853 | 0.9894 | 0.9873 |
| 0.2014 | 9.0 | 17631 | 0.1597 | 0.9853 | 0.9893 | 0.9873 |
| 0.1992 | 10.0 | 19590 | 0.1604 | 0.9877 | 0.9911 | 0.9894 |
| 0.1975 | 11.0 | 21549 | 0.1563 | 0.9853 | 0.9894 | 0.9873 |
| 0.1959 | 12.0 | 23508 | 0.1518 | 0.9853 | 0.9894 | 0.9873 |
| 0.1948 | 13.0 | 25467 | 0.1429 | 0.9902 | 0.9929 | 0.9915 |
| 0.1937 | 14.0 | 27426 | 0.1484 | 0.9853 | 0.9894 | 0.9873 |
| 0.1928 | 15.0 | 29385 | 0.1527 | 0.9804 | 0.9856 | 0.9830 |
| 0.1919 | 16.0 | 31344 | 0.1433 | 0.9926 | 0.9947 | 0.9936 |
| 0.1913 | 17.0 | 33303 | 0.1445 | 0.9902 | 0.9929 | 0.9915 |
| 0.1905 | 18.0 | 35262 | 0.1407 | 0.9926 | 0.9947 | 0.9936 |
| 0.1899 | 19.0 | 37221 | 0.1402 | 0.9926 | 0.9947 | 0.9936 |
| 0.1892 | 20.0 | 39180 | 0.1387 | 0.9926 | 0.9947 | 0.9936 |
| 0.1887 | 21.0 | 41139 | 0.1384 | 0.9926 | 0.9947 | 0.9936 |
| 0.1882 | 22.0 | 43098 | 0.1430 | 0.9951 | 0.9964 | 0.9958 |
| 0.1877 | 23.0 | 45057 | 0.1384 | 0.9951 | 0.9964 | 0.9958 |
| 0.1871 | 24.0 | 47016 | 0.1398 | 0.9951 | 0.9964 | 0.9958 |
| 0.1867 | 25.0 | 48975 | 0.1336 | 0.9926 | 0.9947 | 0.9936 |
| 0.1863 | 26.0 | 50934 | 0.1368 | 0.9951 | 0.9964 | 0.9958 |
| 0.1859 | 27.0 | 52893 | 0.1337 | 0.9951 | 0.9964 | 0.9958 |
| 0.1855 | 28.0 | 54852 | 0.1352 | 0.9926 | 0.9947 | 0.9936 |
| 0.1851 | 29.0 | 56811 | 0.1314 | 0.9951 | 0.9964 | 0.9958 |
| 0.1847 | 30.0 | 58770 | 0.1333 | 0.9951 | 0.9964 | 0.9958 |
| 0.1844 | 31.0 | 60729 | 0.1368 | 0.9951 | 0.9964 | 0.9958 |
| 0.184 | 32.0 | 62688 | 0.1310 | 0.9951 | 0.9964 | 0.9958 |
| 0.1837 | 33.0 | 64647 | 0.1321 | 0.9951 | 0.9964 | 0.9958 |
| 0.1834 | 34.0 | 66606 | 0.1302 | 0.9926 | 0.9947 | 0.9936 |
| 0.183 | 35.0 | 68565 | 0.1320 | 0.9951 | 0.9964 | 0.9958 |
| 0.1827 | 36.0 | 70524 | 0.1303 | 0.9951 | 0.9964 | 0.9958 |
| 0.1825 | 37.0 | 72483 | 0.1273 | 0.9951 | 0.9964 | 0.9958 |
| 0.1822 | 38.0 | 74442 | 0.1293 | 0.9951 | 0.9964 | 0.9958 |
| 0.1819 | 39.0 | 76401 | 0.1296 | 0.9951 | 0.9964 | 0.9958 |
| 0.1817 | 40.0 | 78360 | 0.1305 | 0.9926 | 0.9947 | 0.9936 |
| 0.1814 | 41.0 | 80319 | 0.1267 | 0.9926 | 0.9947 | 0.9936 |
| 0.1812 | 42.0 | 82278 | 0.1267 | 0.9951 | 0.9964 | 0.9958 |
| 0.1809 | 43.0 | 84237 | 0.1278 | 0.9902 | 0.9929 | 0.9915 |
| 0.1807 | 44.0 | 86196 | 0.1293 | 0.9951 | 0.9964 | 0.9958 |
| 0.1805 | 45.0 | 88155 | 0.1269 | 0.9951 | 0.9964 | 0.9958 |
| 0.1803 | 46.0 | 90114 | 0.1284 | 0.9951 | 0.9964 | 0.9958 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
