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20230830022029
This model is a fine-tuned version of bert-large-cased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3320
- Accuracy: 0.6442
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: 0.009
- train_batch_size: 16
- eval_batch_size: 8
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 35 | 0.7064 | 0.4135 |
| No log | 2.0 | 70 | 0.4192 | 0.4423 |
| No log | 3.0 | 105 | 0.5299 | 0.3654 |
| No log | 4.0 | 140 | 0.9673 | 0.4038 |
| No log | 5.0 | 175 | 0.5424 | 0.4038 |
| No log | 6.0 | 210 | 0.3545 | 0.5865 |
| No log | 7.0 | 245 | 0.4776 | 0.3654 |
| No log | 8.0 | 280 | 0.5675 | 0.3942 |
| No log | 9.0 | 315 | 0.4536 | 0.6346 |
| No log | 10.0 | 350 | 0.4334 | 0.3846 |
| No log | 11.0 | 385 | 0.3976 | 0.4327 |
| No log | 12.0 | 420 | 0.7202 | 0.3654 |
| No log | 13.0 | 455 | 0.4091 | 0.3558 |
| No log | 14.0 | 490 | 0.3550 | 0.6346 |
| 0.7045 | 15.0 | 525 | 0.4439 | 0.4038 |
| 0.7045 | 16.0 | 560 | 0.3460 | 0.6346 |
| 0.7045 | 17.0 | 595 | 0.4544 | 0.375 |
| 0.7045 | 18.0 | 630 | 0.7182 | 0.6346 |
| 0.7045 | 19.0 | 665 | 0.3717 | 0.6346 |
| 0.7045 | 20.0 | 700 | 0.6814 | 0.375 |
| 0.7045 | 21.0 | 735 | 0.5718 | 0.6346 |
| 0.7045 | 22.0 | 770 | 0.4977 | 0.3846 |
| 0.7045 | 23.0 | 805 | 0.3439 | 0.6346 |
| 0.7045 | 24.0 | 840 | 0.5466 | 0.6346 |
| 0.7045 | 25.0 | 875 | 0.6025 | 0.6346 |
| 0.7045 | 26.0 | 910 | 0.4123 | 0.6346 |
| 0.7045 | 27.0 | 945 | 0.3434 | 0.5096 |
| 0.7045 | 28.0 | 980 | 0.3336 | 0.6154 |
| 0.4988 | 29.0 | 1015 | 0.8251 | 0.6346 |
| 0.4988 | 30.0 | 1050 | 0.5884 | 0.3654 |
| 0.4988 | 31.0 | 1085 | 0.4073 | 0.3942 |
| 0.4988 | 32.0 | 1120 | 0.5479 | 0.6346 |
| 0.4988 | 33.0 | 1155 | 0.3304 | 0.6346 |
| 0.4988 | 34.0 | 1190 | 0.4936 | 0.3654 |
| 0.4988 | 35.0 | 1225 | 0.3974 | 0.6346 |
| 0.4988 | 36.0 | 1260 | 0.4786 | 0.6346 |
| 0.4988 | 37.0 | 1295 | 0.4672 | 0.3654 |
| 0.4988 | 38.0 | 1330 | 0.4080 | 0.4038 |
| 0.4988 | 39.0 | 1365 | 0.3289 | 0.6442 |
| 0.4988 | 40.0 | 1400 | 0.5053 | 0.3654 |
| 0.4988 | 41.0 | 1435 | 0.3908 | 0.6346 |
| 0.4988 | 42.0 | 1470 | 0.5441 | 0.3846 |
| 0.4887 | 43.0 | 1505 | 0.6210 | 0.3654 |
| 0.4887 | 44.0 | 1540 | 0.3332 | 0.6346 |
| 0.4887 | 45.0 | 1575 | 0.3803 | 0.6346 |
| 0.4887 | 46.0 | 1610 | 0.3663 | 0.4327 |
| 0.4887 | 47.0 | 1645 | 0.3864 | 0.4231 |
| 0.4887 | 48.0 | 1680 | 0.3347 | 0.6346 |
| 0.4887 | 49.0 | 1715 | 0.4551 | 0.4038 |
| 0.4887 | 50.0 | 1750 | 0.3940 | 0.4423 |
| 0.4887 | 51.0 | 1785 | 0.4588 | 0.3942 |
| 0.4887 | 52.0 | 1820 | 0.3578 | 0.4423 |
| 0.4887 | 53.0 | 1855 | 0.5102 | 0.3654 |
| 0.4887 | 54.0 | 1890 | 0.3279 | 0.6538 |
| 0.4887 | 55.0 | 1925 | 0.3605 | 0.4135 |
| 0.4887 | 56.0 | 1960 | 0.3335 | 0.6346 |
| 0.4887 | 57.0 | 1995 | 0.3866 | 0.4135 |
| 0.4258 | 58.0 | 2030 | 0.3303 | 0.6346 |
| 0.4258 | 59.0 | 2065 | 0.3881 | 0.3942 |
| 0.4258 | 60.0 | 2100 | 0.3402 | 0.6346 |
| 0.4258 | 61.0 | 2135 | 0.3387 | 0.6346 |
| 0.4258 | 62.0 | 2170 | 0.3442 | 0.6346 |
| 0.4258 | 63.0 | 2205 | 0.3354 | 0.6346 |
| 0.4258 | 64.0 | 2240 | 0.3334 | 0.6538 |
| 0.4258 | 65.0 | 2275 | 0.4705 | 0.6346 |
| 0.4258 | 66.0 | 2310 | 0.3418 | 0.6346 |
| 0.4258 | 67.0 | 2345 | 0.3467 | 0.4519 |
| 0.4258 | 68.0 | 2380 | 0.3349 | 0.6346 |
| 0.4258 | 69.0 | 2415 | 0.3323 | 0.6346 |
| 0.4258 | 70.0 | 2450 | 0.3560 | 0.6346 |
| 0.4258 | 71.0 | 2485 | 0.3436 | 0.5096 |
| 0.3827 | 72.0 | 2520 | 0.3333 | 0.6154 |
| 0.3827 | 73.0 | 2555 | 0.3287 | 0.6538 |
| 0.3827 | 74.0 | 2590 | 0.3350 | 0.6154 |
| 0.3827 | 75.0 | 2625 | 0.3290 | 0.6346 |
| 0.3827 | 76.0 | 2660 | 0.3337 | 0.6346 |
| 0.3827 | 77.0 | 2695 | 0.3383 | 0.5865 |
| 0.3827 | 78.0 | 2730 | 0.3297 | 0.6346 |
| 0.3827 | 79.0 | 2765 | 0.3299 | 0.6346 |
| 0.3827 | 80.0 | 2800 | 0.3320 | 0.6442 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
