2_5e-3_20_0.1
This model is a fine-tuned version of bert-large-uncased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6966
- Accuracy: 0.7407
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: 0.005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.2231 | 1.0 | 590 | 1.2340 | 0.3789 |
1.2295 | 2.0 | 1180 | 1.2339 | 0.3798 |
1.0628 | 3.0 | 1770 | 1.3715 | 0.3823 |
1.0865 | 4.0 | 2360 | 1.9743 | 0.3783 |
1.0975 | 5.0 | 2950 | 0.9219 | 0.5908 |
0.9667 | 6.0 | 3540 | 0.8883 | 0.6465 |
0.9542 | 7.0 | 4130 | 1.1371 | 0.5211 |
0.9021 | 8.0 | 4720 | 0.8855 | 0.6703 |
0.8629 | 9.0 | 5310 | 0.8316 | 0.6841 |
0.824 | 10.0 | 5900 | 0.9914 | 0.6596 |
0.8085 | 11.0 | 6490 | 0.8443 | 0.6908 |
0.7644 | 12.0 | 7080 | 0.8058 | 0.6706 |
0.765 | 13.0 | 7670 | 0.7726 | 0.7 |
0.7438 | 14.0 | 8260 | 0.8309 | 0.6887 |
0.7459 | 15.0 | 8850 | 0.7637 | 0.7018 |
0.717 | 16.0 | 9440 | 0.8887 | 0.6254 |
0.6932 | 17.0 | 10030 | 0.7578 | 0.6991 |
0.7052 | 18.0 | 10620 | 0.7760 | 0.7049 |
0.6814 | 19.0 | 11210 | 0.7195 | 0.7162 |
0.7066 | 20.0 | 11800 | 0.7185 | 0.7239 |
0.6685 | 21.0 | 12390 | 0.7384 | 0.7196 |
0.673 | 22.0 | 12980 | 0.7108 | 0.7239 |
0.6678 | 23.0 | 13570 | 0.7177 | 0.7260 |
0.6494 | 24.0 | 14160 | 0.6995 | 0.7248 |
0.6415 | 25.0 | 14750 | 0.7502 | 0.7336 |
0.6456 | 26.0 | 15340 | 0.7096 | 0.7205 |
0.6303 | 27.0 | 15930 | 0.7382 | 0.7061 |
0.6168 | 28.0 | 16520 | 0.7049 | 0.7379 |
0.6076 | 29.0 | 17110 | 0.7018 | 0.7232 |
0.6083 | 30.0 | 17700 | 0.7522 | 0.7190 |
0.5955 | 31.0 | 18290 | 0.6889 | 0.7306 |
0.5929 | 32.0 | 18880 | 0.7513 | 0.7281 |
0.5827 | 33.0 | 19470 | 0.6930 | 0.7446 |
0.5727 | 34.0 | 20060 | 0.6848 | 0.7355 |
0.5557 | 35.0 | 20650 | 0.7043 | 0.7260 |
0.572 | 36.0 | 21240 | 0.6876 | 0.7367 |
0.5564 | 37.0 | 21830 | 0.6957 | 0.7394 |
0.5454 | 38.0 | 22420 | 0.7031 | 0.7275 |
0.5471 | 39.0 | 23010 | 0.6980 | 0.7367 |
0.5323 | 40.0 | 23600 | 0.7033 | 0.7382 |
0.5439 | 41.0 | 24190 | 0.7215 | 0.7205 |
0.5332 | 42.0 | 24780 | 0.6841 | 0.7401 |
0.5275 | 43.0 | 25370 | 0.6904 | 0.7413 |
0.5263 | 44.0 | 25960 | 0.7266 | 0.7248 |
0.5238 | 45.0 | 26550 | 0.6961 | 0.7428 |
0.5165 | 46.0 | 27140 | 0.7033 | 0.7330 |
0.5126 | 47.0 | 27730 | 0.6928 | 0.7425 |
0.5148 | 48.0 | 28320 | 0.6859 | 0.7413 |
0.5141 | 49.0 | 28910 | 0.6945 | 0.7379 |
0.4973 | 50.0 | 29500 | 0.6952 | 0.7391 |
0.5043 | 51.0 | 30090 | 0.6954 | 0.7364 |
0.4966 | 52.0 | 30680 | 0.6890 | 0.7376 |
0.4967 | 53.0 | 31270 | 0.6937 | 0.7428 |
0.4974 | 54.0 | 31860 | 0.7009 | 0.7370 |
0.4977 | 55.0 | 32450 | 0.6961 | 0.7398 |
0.4948 | 56.0 | 33040 | 0.6986 | 0.7391 |
0.479 | 57.0 | 33630 | 0.6919 | 0.7407 |
0.4835 | 58.0 | 34220 | 0.6965 | 0.7440 |
0.4811 | 59.0 | 34810 | 0.6962 | 0.7419 |
0.485 | 60.0 | 35400 | 0.6966 | 0.7407 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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