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bert-base-cased-finetuned-semeval2017-MLM
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4287
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6571 | 1.0 | 92 | 2.4968 |
| 2.5201 | 2.0 | 184 | 2.3410 |
| 2.3929 | 3.0 | 276 | 2.3291 |
| 2.3975 | 4.0 | 368 | 2.3775 |
| 2.3432 | 5.0 | 460 | 2.3056 |
| 2.2667 | 6.0 | 552 | 2.2628 |
| 2.2044 | 7.0 | 644 | 2.2375 |
| 2.122 | 8.0 | 736 | 2.3169 |
| 2.1331 | 9.0 | 828 | 2.2612 |
| 2.1225 | 10.0 | 920 | 2.2566 |
| 2.0243 | 11.0 | 1012 | 2.2638 |
| 1.9822 | 12.0 | 1104 | 2.2491 |
| 1.9631 | 13.0 | 1196 | 2.1650 |
| 1.9647 | 14.0 | 1288 | 2.2896 |
| 1.9628 | 15.0 | 1380 | 2.2677 |
| 1.8943 | 16.0 | 1472 | 2.2228 |
| 1.8406 | 17.0 | 1564 | 2.2152 |
| 1.8349 | 18.0 | 1656 | 2.1937 |
| 1.7881 | 19.0 | 1748 | 2.2720 |
| 1.8219 | 20.0 | 1840 | 2.2433 |
| 1.762 | 21.0 | 1932 | 2.2314 |
| 1.7639 | 22.0 | 2024 | 2.3166 |
| 1.7431 | 23.0 | 2116 | 2.3272 |
| 1.7016 | 24.0 | 2208 | 2.3099 |
| 1.7155 | 25.0 | 2300 | 2.2311 |
| 1.6764 | 26.0 | 2392 | 2.2315 |
| 1.6664 | 27.0 | 2484 | 2.3288 |
| 1.6264 | 28.0 | 2576 | 2.2736 |
| 1.6485 | 29.0 | 2668 | 2.4059 |
| 1.5953 | 30.0 | 2760 | 2.2846 |
| 1.5861 | 31.0 | 2852 | 2.2768 |
| 1.5561 | 32.0 | 2944 | 2.2790 |
| 1.52 | 33.0 | 3036 | 2.3495 |
| 1.5191 | 34.0 | 3128 | 2.3247 |
| 1.5188 | 35.0 | 3220 | 2.2899 |
| 1.5309 | 36.0 | 3312 | 2.2977 |
| 1.4596 | 37.0 | 3404 | 2.3863 |
| 1.4721 | 38.0 | 3496 | 2.3478 |
| 1.4287 | 39.0 | 3588 | 2.4118 |
| 1.4523 | 40.0 | 3680 | 2.3188 |
| 1.4227 | 41.0 | 3772 | 2.2983 |
| 1.3878 | 42.0 | 3864 | 2.3854 |
| 1.3953 | 43.0 | 3956 | 2.3611 |
| 1.3933 | 44.0 | 4048 | 2.4200 |
| 1.3936 | 45.0 | 4140 | 2.5302 |
| 1.3838 | 46.0 | 4232 | 2.3461 |
| 1.3474 | 47.0 | 4324 | 2.4587 |
| 1.3393 | 48.0 | 4416 | 2.4109 |
| 1.3802 | 49.0 | 4508 | 2.4339 |
| 1.2951 | 50.0 | 4600 | 2.3778 |
| 1.3027 | 51.0 | 4692 | 2.3809 |
| 1.2994 | 52.0 | 4784 | 2.3793 |
| 1.2681 | 53.0 | 4876 | 2.3298 |
| 1.3187 | 54.0 | 4968 | 2.3417 |
| 1.2634 | 55.0 | 5060 | 2.4025 |
| 1.2539 | 56.0 | 5152 | 2.4274 |
| 1.1917 | 57.0 | 5244 | 2.3993 |
| 1.1824 | 58.0 | 5336 | 2.5562 |
| 1.182 | 59.0 | 5428 | 2.4378 |
| 1.1575 | 60.0 | 5520 | 2.4162 |
| 1.1797 | 61.0 | 5612 | 2.4015 |
| 1.2231 | 62.0 | 5704 | 2.3371 |
| 1.2202 | 63.0 | 5796 | 2.3360 |
| 1.1704 | 64.0 | 5888 | 2.4306 |
| 1.1405 | 65.0 | 5980 | 2.3808 |
| 1.1265 | 66.0 | 6072 | 2.4630 |
| 1.1783 | 67.0 | 6164 | 2.4870 |
| 1.089 | 68.0 | 6256 | 2.4710 |
| 1.1336 | 69.0 | 6348 | 2.5672 |
| 1.1555 | 70.0 | 6440 | 2.5471 |
| 1.104 | 71.0 | 6532 | 2.4424 |
| 1.1231 | 72.0 | 6624 | 2.4411 |
| 1.0911 | 73.0 | 6716 | 2.4051 |
| 1.0946 | 74.0 | 6808 | 2.4894 |
| 1.0729 | 75.0 | 6900 | 2.4341 |
| 1.0714 | 76.0 | 6992 | 2.4806 |
| 1.1039 | 77.0 | 7084 | 2.4074 |
| 1.0667 | 78.0 | 7176 | 2.3871 |
| 1.083 | 79.0 | 7268 | 2.5811 |
| 1.0563 | 80.0 | 7360 | 2.4485 |
| 1.0875 | 81.0 | 7452 | 2.4285 |
| 1.0275 | 82.0 | 7544 | 2.5647 |
| 1.0217 | 83.0 | 7636 | 2.5082 |
| 1.0487 | 84.0 | 7728 | 2.5451 |
| 1.0363 | 85.0 | 7820 | 2.4841 |
| 1.0527 | 86.0 | 7912 | 2.5876 |
| 1.0739 | 87.0 | 8004 | 2.4595 |
| 1.0488 | 88.0 | 8096 | 2.5566 |
| 1.0792 | 89.0 | 8188 | 2.3546 |
| 1.0418 | 90.0 | 8280 | 2.4778 |
| 1.0056 | 91.0 | 8372 | 2.4825 |
| 1.0491 | 92.0 | 8464 | 2.4605 |
| 1.009 | 93.0 | 8556 | 2.4413 |
| 1.0 | 94.0 | 8648 | 2.5231 |
| 0.9951 | 95.0 | 8740 | 2.5699 |
| 1.0585 | 96.0 | 8832 | 2.4249 |
| 1.052 | 97.0 | 8924 | 2.5151 |
| 1.0241 | 98.0 | 9016 | 2.4756 |
| 1.014 | 99.0 | 9108 | 2.4787 |
| 1.0623 | 100.0 | 9200 | 2.6042 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
