bert_uncased_L-2_H-128_A-2-finetuned-parsed
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.2883
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: 200
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 59 | 4.6900 |
| No log | 2.0 | 118 | 4.6347 |
| No log | 3.0 | 177 | 4.6578 |
| No log | 4.0 | 236 | 4.5731 |
| No log | 5.0 | 295 | 4.6258 |
| No log | 6.0 | 354 | 4.6365 |
| No log | 7.0 | 413 | 4.7292 |
| No log | 8.0 | 472 | 4.4789 |
| 4.5634 | 9.0 | 531 | 4.3161 |
| 4.5634 | 10.0 | 590 | 4.6929 |
| 4.5634 | 11.0 | 649 | 4.5543 |
| 4.5634 | 12.0 | 708 | 4.3739 |
| 4.5634 | 13.0 | 767 | 4.6118 |
| 4.5634 | 14.0 | 826 | 4.4036 |
| 4.5634 | 15.0 | 885 | 4.3940 |
| 4.5634 | 16.0 | 944 | 4.5944 |
| 4.0896 | 17.0 | 1003 | 4.3630 |
| 4.0896 | 18.0 | 1062 | 4.0447 |
| 4.0896 | 19.0 | 1121 | 4.3832 |
| 4.0896 | 20.0 | 1180 | 4.0535 |
| 4.0896 | 21.0 | 1239 | 4.5213 |
| 4.0896 | 22.0 | 1298 | 4.5887 |
| 4.0896 | 23.0 | 1357 | 4.5211 |
| 4.0896 | 24.0 | 1416 | 4.1876 |
| 4.0896 | 25.0 | 1475 | 4.5861 |
| 3.9145 | 26.0 | 1534 | 4.3581 |
| 3.9145 | 27.0 | 1593 | 4.6545 |
| 3.9145 | 28.0 | 1652 | 4.4919 |
| 3.9145 | 29.0 | 1711 | 4.1109 |
| 3.9145 | 30.0 | 1770 | 4.2736 |
| 3.9145 | 31.0 | 1829 | 4.6461 |
| 3.9145 | 32.0 | 1888 | 4.3111 |
| 3.9145 | 33.0 | 1947 | 4.2909 |
| 3.8088 | 34.0 | 2006 | 4.1168 |
| 3.8088 | 35.0 | 2065 | 4.2329 |
| 3.8088 | 36.0 | 2124 | 4.5285 |
| 3.8088 | 37.0 | 2183 | 4.4841 |
| 3.8088 | 38.0 | 2242 | 4.2489 |
| 3.8088 | 39.0 | 2301 | 4.2384 |
| 3.8088 | 40.0 | 2360 | 4.3610 |
| 3.8088 | 41.0 | 2419 | 4.2758 |
| 3.8088 | 42.0 | 2478 | 4.2895 |
| 3.7034 | 43.0 | 2537 | 4.2824 |
| 3.7034 | 44.0 | 2596 | 4.4997 |
| 3.7034 | 45.0 | 2655 | 4.5091 |
| 3.7034 | 46.0 | 2714 | 4.0883 |
| 3.7034 | 47.0 | 2773 | 4.2018 |
| 3.7034 | 48.0 | 2832 | 4.3701 |
| 3.7034 | 49.0 | 2891 | 4.0764 |
| 3.7034 | 50.0 | 2950 | 4.6149 |
| 3.6455 | 51.0 | 3009 | 4.3629 |
| 3.6455 | 52.0 | 3068 | 4.2199 |
| 3.6455 | 53.0 | 3127 | 4.3543 |
| 3.6455 | 54.0 | 3186 | 4.7006 |
| 3.6455 | 55.0 | 3245 | 4.1633 |
| 3.6455 | 56.0 | 3304 | 4.5183 |
| 3.6455 | 57.0 | 3363 | 4.1918 |
| 3.6455 | 58.0 | 3422 | 4.4810 |
| 3.6455 | 59.0 | 3481 | 4.1398 |
| 3.5468 | 60.0 | 3540 | 3.9632 |
| 3.5468 | 61.0 | 3599 | 4.4640 |
| 3.5468 | 62.0 | 3658 | 4.0500 |
| 3.5468 | 63.0 | 3717 | 4.3956 |
| 3.5468 | 64.0 | 3776 | 4.3922 |
| 3.5468 | 65.0 | 3835 | 4.2513 |
| 3.5468 | 66.0 | 3894 | 4.4475 |
| 3.5468 | 67.0 | 3953 | 4.3037 |
| 3.4975 | 68.0 | 4012 | 4.1568 |
| 3.4975 | 69.0 | 4071 | 4.2253 |
| 3.4975 | 70.0 | 4130 | 4.1202 |
| 3.4975 | 71.0 | 4189 | 4.4421 |
| 3.4975 | 72.0 | 4248 | 4.3548 |
| 3.4975 | 73.0 | 4307 | 4.1671 |
| 3.4975 | 74.0 | 4366 | 4.4090 |
| 3.4975 | 75.0 | 4425 | 4.1064 |
| 3.4975 | 76.0 | 4484 | 4.2109 |
| 3.44 | 77.0 | 4543 | 4.3244 |
| 3.44 | 78.0 | 4602 | 4.1995 |
| 3.44 | 79.0 | 4661 | 4.4518 |
| 3.44 | 80.0 | 4720 | 4.1991 |
| 3.44 | 81.0 | 4779 | 4.4183 |
| 3.44 | 82.0 | 4838 | 4.2173 |
| 3.44 | 83.0 | 4897 | 4.1721 |
| 3.44 | 84.0 | 4956 | 4.1931 |
| 3.3916 | 85.0 | 5015 | 4.3280 |
| 3.3916 | 86.0 | 5074 | 4.3347 |
| 3.3916 | 87.0 | 5133 | 4.3243 |
| 3.3916 | 88.0 | 5192 | 4.2708 |
| 3.3916 | 89.0 | 5251 | 4.1580 |
| 3.3916 | 90.0 | 5310 | 4.0348 |
| 3.3916 | 91.0 | 5369 | 4.0605 |
| 3.3916 | 92.0 | 5428 | 4.2083 |
| 3.3916 | 93.0 | 5487 | 4.2378 |
| 3.3817 | 94.0 | 5546 | 4.2171 |
| 3.3817 | 95.0 | 5605 | 3.9581 |
| 3.3817 | 96.0 | 5664 | 4.1668 |
| 3.3817 | 97.0 | 5723 | 4.0394 |
| 3.3817 | 98.0 | 5782 | 4.2231 |
| 3.3817 | 99.0 | 5841 | 4.1900 |
| 3.3817 | 100.0 | 5900 | 4.3041 |
| 3.3817 | 101.0 | 5959 | 4.3827 |
| 3.3526 | 102.0 | 6018 | 4.0975 |
| 3.3526 | 103.0 | 6077 | 4.3543 |
| 3.3526 | 104.0 | 6136 | 4.2104 |
| 3.3526 | 105.0 | 6195 | 4.2408 |
| 3.3526 | 106.0 | 6254 | 4.4281 |
| 3.3526 | 107.0 | 6313 | 4.4816 |
| 3.3526 | 108.0 | 6372 | 4.1995 |
| 3.3526 | 109.0 | 6431 | 4.1844 |
| 3.3526 | 110.0 | 6490 | 4.2414 |
| 3.3035 | 111.0 | 6549 | 4.3478 |
| 3.3035 | 112.0 | 6608 | 3.9579 |
| 3.3035 | 113.0 | 6667 | 4.2558 |
| 3.3035 | 114.0 | 6726 | 4.0050 |
| 3.3035 | 115.0 | 6785 | 4.1944 |
| 3.3035 | 116.0 | 6844 | 4.0384 |
| 3.3035 | 117.0 | 6903 | 4.5749 |
| 3.3035 | 118.0 | 6962 | 4.3816 |
| 3.2884 | 119.0 | 7021 | 4.0829 |
| 3.2884 | 120.0 | 7080 | 4.1100 |
| 3.2884 | 121.0 | 7139 | 4.3181 |
| 3.2884 | 122.0 | 7198 | 4.2051 |
| 3.2884 | 123.0 | 7257 | 4.1495 |
| 3.2884 | 124.0 | 7316 | 4.2398 |
| 3.2884 | 125.0 | 7375 | 4.2553 |
| 3.2884 | 126.0 | 7434 | 4.0788 |
| 3.2884 | 127.0 | 7493 | 4.4999 |
| 3.2817 | 128.0 | 7552 | 4.4331 |
| 3.2817 | 129.0 | 7611 | 4.3983 |
| 3.2817 | 130.0 | 7670 | 4.1597 |
| 3.2817 | 131.0 | 7729 | 4.2732 |
| 3.2817 | 132.0 | 7788 | 4.1203 |
| 3.2817 | 133.0 | 7847 | 4.4417 |
| 3.2817 | 134.0 | 7906 | 4.0591 |
| 3.2817 | 135.0 | 7965 | 4.0435 |
| 3.252 | 136.0 | 8024 | 4.0461 |
| 3.252 | 137.0 | 8083 | 4.2521 |
| 3.252 | 138.0 | 8142 | 4.2749 |
| 3.252 | 139.0 | 8201 | 4.1346 |
| 3.252 | 140.0 | 8260 | 4.0411 |
| 3.252 | 141.0 | 8319 | 4.0656 |
| 3.252 | 142.0 | 8378 | 4.3978 |
| 3.252 | 143.0 | 8437 | 4.0533 |
| 3.252 | 144.0 | 8496 | 3.9734 |
| 3.217 | 145.0 | 8555 | 4.2113 |
| 3.217 | 146.0 | 8614 | 4.5480 |
| 3.217 | 147.0 | 8673 | 4.1805 |
| 3.217 | 148.0 | 8732 | 4.2144 |
| 3.217 | 149.0 | 8791 | 4.1457 |
| 3.217 | 150.0 | 8850 | 4.3311 |
| 3.217 | 151.0 | 8909 | 4.1565 |
| 3.217 | 152.0 | 8968 | 4.3584 |
| 3.2183 | 153.0 | 9027 | 4.3837 |
| 3.2183 | 154.0 | 9086 | 4.0912 |
| 3.2183 | 155.0 | 9145 | 4.0785 |
| 3.2183 | 156.0 | 9204 | 4.2501 |
| 3.2183 | 157.0 | 9263 | 4.1515 |
| 3.2183 | 158.0 | 9322 | 4.0559 |
| 3.2183 | 159.0 | 9381 | 3.9969 |
| 3.2183 | 160.0 | 9440 | 4.0528 |
| 3.2183 | 161.0 | 9499 | 3.9618 |
| 3.2109 | 162.0 | 9558 | 4.2596 |
| 3.2109 | 163.0 | 9617 | 4.0760 |
| 3.2109 | 164.0 | 9676 | 4.2589 |
| 3.2109 | 165.0 | 9735 | 4.2227 |
| 3.2109 | 166.0 | 9794 | 4.3354 |
| 3.2109 | 167.0 | 9853 | 4.3471 |
| 3.2109 | 168.0 | 9912 | 4.1578 |
| 3.2109 | 169.0 | 9971 | 4.4163 |
| 3.1868 | 170.0 | 10030 | 4.0754 |
| 3.1868 | 171.0 | 10089 | 4.2543 |
| 3.1868 | 172.0 | 10148 | 3.9498 |
| 3.1868 | 173.0 | 10207 | 4.0863 |
| 3.1868 | 174.0 | 10266 | 4.3090 |
| 3.1868 | 175.0 | 10325 | 4.2731 |
| 3.1868 | 176.0 | 10384 | 4.1997 |
| 3.1868 | 177.0 | 10443 | 4.2273 |
| 3.1905 | 178.0 | 10502 | 4.3560 |
| 3.1905 | 179.0 | 10561 | 4.3330 |
| 3.1905 | 180.0 | 10620 | 4.1770 |
| 3.1905 | 181.0 | 10679 | 3.8779 |
| 3.1905 | 182.0 | 10738 | 4.2199 |
| 3.1905 | 183.0 | 10797 | 4.1409 |
| 3.1905 | 184.0 | 10856 | 4.3601 |
| 3.1905 | 185.0 | 10915 | 4.2380 |
| 3.1905 | 186.0 | 10974 | 4.4688 |
| 3.1774 | 187.0 | 11033 | 4.2305 |
| 3.1774 | 188.0 | 11092 | 3.9129 |
| 3.1774 | 189.0 | 11151 | 4.2889 |
| 3.1774 | 190.0 | 11210 | 3.8790 |
| 3.1774 | 191.0 | 11269 | 4.4458 |
| 3.1774 | 192.0 | 11328 | 4.2899 |
| 3.1774 | 193.0 | 11387 | 4.4378 |
| 3.1774 | 194.0 | 11446 | 4.2316 |
| 3.179 | 195.0 | 11505 | 4.0360 |
| 3.179 | 196.0 | 11564 | 4.1284 |
| 3.179 | 197.0 | 11623 | 4.3879 |
| 3.179 | 198.0 | 11682 | 4.0715 |
| 3.179 | 199.0 | 11741 | 4.1888 |
| 3.179 | 200.0 | 11800 | 4.3268 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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