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1_7e-3_5_0.5
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: 0.7994
- Accuracy: 0.7590
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: 0.007
- 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: 100.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.4468 | 1.0 | 590 | 2.2373 | 0.6183 |
2.615 | 2.0 | 1180 | 1.8655 | 0.5557 |
2.2782 | 3.0 | 1770 | 1.8976 | 0.5260 |
1.7962 | 4.0 | 2360 | 1.7110 | 0.5746 |
1.6241 | 5.0 | 2950 | 1.4946 | 0.6801 |
1.4269 | 6.0 | 3540 | 1.3572 | 0.6972 |
1.4106 | 7.0 | 4130 | 1.3887 | 0.6394 |
1.3024 | 8.0 | 4720 | 1.2780 | 0.6966 |
1.2769 | 9.0 | 5310 | 1.1492 | 0.6896 |
1.1959 | 10.0 | 5900 | 1.4278 | 0.6936 |
1.1842 | 11.0 | 6490 | 1.0641 | 0.7156 |
1.103 | 12.0 | 7080 | 1.0075 | 0.7232 |
1.0823 | 13.0 | 7670 | 1.0099 | 0.7086 |
1.0542 | 14.0 | 8260 | 1.0171 | 0.7294 |
1.0489 | 15.0 | 8850 | 0.9553 | 0.7297 |
1.0048 | 16.0 | 9440 | 0.9329 | 0.7336 |
0.9169 | 17.0 | 10030 | 0.9543 | 0.7321 |
0.9179 | 18.0 | 10620 | 0.9167 | 0.7327 |
0.8928 | 19.0 | 11210 | 0.9433 | 0.7404 |
0.8929 | 20.0 | 11800 | 1.0377 | 0.7346 |
0.8262 | 21.0 | 12390 | 0.8871 | 0.7440 |
0.8508 | 22.0 | 12980 | 0.9002 | 0.7434 |
0.8101 | 23.0 | 13570 | 0.8907 | 0.7471 |
0.7787 | 24.0 | 14160 | 0.8993 | 0.7471 |
0.7706 | 25.0 | 14750 | 0.8341 | 0.7440 |
0.7485 | 26.0 | 15340 | 0.8837 | 0.7376 |
0.7498 | 27.0 | 15930 | 0.8711 | 0.7385 |
0.7175 | 28.0 | 16520 | 0.9197 | 0.7495 |
0.7034 | 29.0 | 17110 | 0.8367 | 0.7434 |
0.685 | 30.0 | 17700 | 0.8322 | 0.7459 |
0.6718 | 31.0 | 18290 | 0.8840 | 0.7474 |
0.6746 | 32.0 | 18880 | 0.8978 | 0.7492 |
0.6579 | 33.0 | 19470 | 0.8499 | 0.7456 |
0.6305 | 34.0 | 20060 | 0.8291 | 0.7480 |
0.6316 | 35.0 | 20650 | 0.8555 | 0.7385 |
0.6198 | 36.0 | 21240 | 0.8694 | 0.7557 |
0.616 | 37.0 | 21830 | 0.8268 | 0.7599 |
0.6331 | 38.0 | 22420 | 0.8227 | 0.7505 |
0.6077 | 39.0 | 23010 | 0.9053 | 0.7554 |
0.5947 | 40.0 | 23600 | 0.9019 | 0.7554 |
0.5773 | 41.0 | 24190 | 0.8128 | 0.7584 |
0.57 | 42.0 | 24780 | 0.8028 | 0.7609 |
0.5686 | 43.0 | 25370 | 0.8444 | 0.7621 |
0.564 | 44.0 | 25960 | 0.8285 | 0.7459 |
0.5584 | 45.0 | 26550 | 0.8303 | 0.7544 |
0.5408 | 46.0 | 27140 | 0.8650 | 0.7560 |
0.54 | 47.0 | 27730 | 0.8684 | 0.7370 |
0.528 | 48.0 | 28320 | 0.8171 | 0.7581 |
0.5499 | 49.0 | 28910 | 0.8792 | 0.7550 |
0.5295 | 50.0 | 29500 | 0.8192 | 0.7578 |
0.5138 | 51.0 | 30090 | 0.8493 | 0.7578 |
0.516 | 52.0 | 30680 | 0.8111 | 0.7581 |
0.5066 | 53.0 | 31270 | 0.8026 | 0.7514 |
0.5061 | 54.0 | 31860 | 0.8134 | 0.7609 |
0.5061 | 55.0 | 32450 | 0.8229 | 0.7618 |
0.4903 | 56.0 | 33040 | 0.8253 | 0.7590 |
0.4876 | 57.0 | 33630 | 0.8467 | 0.7596 |
0.4842 | 58.0 | 34220 | 0.8295 | 0.7566 |
0.4743 | 59.0 | 34810 | 0.8587 | 0.7385 |
0.484 | 60.0 | 35400 | 0.7973 | 0.7550 |
0.4686 | 61.0 | 35990 | 0.8244 | 0.7593 |
0.4734 | 62.0 | 36580 | 0.8127 | 0.7615 |
0.4655 | 63.0 | 37170 | 0.8271 | 0.7529 |
0.457 | 64.0 | 37760 | 0.7995 | 0.7544 |
0.4643 | 65.0 | 38350 | 0.8315 | 0.7642 |
0.4535 | 66.0 | 38940 | 0.8044 | 0.7575 |
0.4445 | 67.0 | 39530 | 0.8785 | 0.7602 |
0.4546 | 68.0 | 40120 | 0.7933 | 0.7587 |
0.4427 | 69.0 | 40710 | 0.8548 | 0.7602 |
0.4441 | 70.0 | 41300 | 0.8274 | 0.7627 |
0.4514 | 71.0 | 41890 | 0.7980 | 0.7495 |
0.4468 | 72.0 | 42480 | 0.8562 | 0.7572 |
0.415 | 73.0 | 43070 | 0.8126 | 0.7636 |
0.4225 | 74.0 | 43660 | 0.8120 | 0.7596 |
0.4372 | 75.0 | 44250 | 0.8545 | 0.7602 |
0.4295 | 76.0 | 44840 | 0.8148 | 0.7462 |
0.4351 | 77.0 | 45430 | 0.8043 | 0.7642 |
0.4379 | 78.0 | 46020 | 0.7927 | 0.7621 |
0.4282 | 79.0 | 46610 | 0.7931 | 0.7624 |
0.4169 | 80.0 | 47200 | 0.8081 | 0.7596 |
0.4142 | 81.0 | 47790 | 0.8231 | 0.7602 |
0.4149 | 82.0 | 48380 | 0.8266 | 0.7602 |
0.409 | 83.0 | 48970 | 0.8020 | 0.7593 |
0.4084 | 84.0 | 49560 | 0.8396 | 0.7621 |
0.4012 | 85.0 | 50150 | 0.8049 | 0.7606 |
0.4056 | 86.0 | 50740 | 0.7971 | 0.7566 |
0.3991 | 87.0 | 51330 | 0.8462 | 0.7599 |
0.4019 | 88.0 | 51920 | 0.8056 | 0.7569 |
0.394 | 89.0 | 52510 | 0.8047 | 0.7554 |
0.3985 | 90.0 | 53100 | 0.8150 | 0.7609 |
0.3978 | 91.0 | 53690 | 0.8178 | 0.7606 |
0.4036 | 92.0 | 54280 | 0.7915 | 0.7560 |
0.3859 | 93.0 | 54870 | 0.8072 | 0.7599 |
0.4053 | 94.0 | 55460 | 0.8112 | 0.7606 |
0.3889 | 95.0 | 56050 | 0.8010 | 0.7587 |
0.3866 | 96.0 | 56640 | 0.8017 | 0.7578 |
0.3806 | 97.0 | 57230 | 0.7965 | 0.7584 |
0.3816 | 98.0 | 57820 | 0.7979 | 0.7590 |
0.3791 | 99.0 | 58410 | 0.7982 | 0.7575 |
0.3782 | 100.0 | 59000 | 0.7994 | 0.7590 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3