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maximuspowers
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bert-philosophy-classifier

Text Classification
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bert
fill-mask
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  1. README.md +43 -34
  2. all_results.json +5 -17
  3. model.safetensors +1 -1
  4. runs/Jun18_00-43-06_7248b5eeee0d/events.out.tfevents.1750207388.7248b5eeee0d.3526.0 +2 -2
  5. train_results.json +5 -5
README.md CHANGED
@@ -16,15 +16,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [maximuspowers/bert-philosophy-adapted](https://huggingface.co/maximuspowers/bert-philosophy-adapted) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5930
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- - Exact Match Accuracy: 0.4138
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- - Macro Precision: 0.3910
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- - Macro Recall: 0.2935
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- - Macro F1: 0.3205
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- - Micro Precision: 0.8062
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- - Micro Recall: 0.5149
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- - Micro F1: 0.6284
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- - Hamming Loss: 0.0499
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  ## Model description
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@@ -59,31 +59,40 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Exact Match Accuracy | Macro Precision | Macro Recall | Macro F1 | Micro Precision | Micro Recall | Micro F1 | Hamming Loss |
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  |:-------------:|:-------:|:----:|:---------------:|:--------------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------:|
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- | 1.8162 | 1.3724 | 100 | 0.9425 | 0.0069 | 0.0588 | 0.0013 | 0.0025 | 0.2 | 0.0050 | 0.0097 | 0.0828 |
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- | 1.3347 | 2.7448 | 200 | 0.7476 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0815 |
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- | 1.1131 | 4.1103 | 300 | 0.7062 | 0.0207 | 0.0588 | 0.0075 | 0.0133 | 1.0 | 0.0299 | 0.0580 | 0.0791 |
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- | 0.9328 | 5.4828 | 400 | 0.6182 | 0.1034 | 0.0588 | 0.0263 | 0.0363 | 1.0 | 0.1045 | 0.1892 | 0.0730 |
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- | 0.9142 | 6.8552 | 500 | 0.5719 | 0.2 | 0.1176 | 0.0661 | 0.0846 | 1.0 | 0.2189 | 0.3592 | 0.0637 |
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- | 0.8329 | 8.2207 | 600 | 0.6049 | 0.2621 | 0.1718 | 0.1076 | 0.1318 | 0.9649 | 0.2736 | 0.4264 | 0.0600 |
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- | 0.7638 | 9.5931 | 700 | 0.6275 | 0.2897 | 0.2302 | 0.1285 | 0.1500 | 0.9667 | 0.2886 | 0.4444 | 0.0588 |
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- | 0.6805 | 10.9655 | 800 | 0.5676 | 0.2828 | 0.2206 | 0.1411 | 0.1529 | 0.8919 | 0.3284 | 0.48 | 0.0580 |
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- | 0.6449 | 12.3310 | 900 | 0.5610 | 0.2966 | 0.1987 | 0.1484 | 0.1645 | 0.8519 | 0.3433 | 0.4894 | 0.0584 |
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- | 0.6151 | 13.7034 | 1000 | 0.5707 | 0.3241 | 0.2613 | 0.1556 | 0.1790 | 0.8780 | 0.3582 | 0.5088 | 0.0564 |
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- | 0.5713 | 15.0690 | 1100 | 0.5384 | 0.3586 | 0.2281 | 0.1705 | 0.1881 | 0.8316 | 0.3930 | 0.5338 | 0.0560 |
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- | 0.5272 | 16.4414 | 1200 | 0.5652 | 0.3655 | 0.3467 | 0.1968 | 0.2230 | 0.7928 | 0.4378 | 0.5641 | 0.0552 |
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- | 0.477 | 17.8138 | 1300 | 0.5931 | 0.3655 | 0.3196 | 0.2090 | 0.2326 | 0.7731 | 0.4577 | 0.575 | 0.0552 |
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- | 0.4533 | 19.1793 | 1400 | 0.5249 | 0.3862 | 0.3520 | 0.2328 | 0.2694 | 0.8496 | 0.4776 | 0.6115 | 0.0495 |
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- | 0.4087 | 20.5517 | 1500 | 0.4931 | 0.4138 | 0.3420 | 0.2410 | 0.2709 | 0.8279 | 0.5025 | 0.6254 | 0.0491 |
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- | 0.4131 | 21.9241 | 1600 | 0.5264 | 0.3862 | 0.3397 | 0.2215 | 0.2527 | 0.8174 | 0.4677 | 0.5949 | 0.0519 |
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- | 0.366 | 23.2897 | 1700 | 0.5911 | 0.4 | 0.3282 | 0.2348 | 0.2608 | 0.7953 | 0.5025 | 0.6159 | 0.0511 |
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- | 0.3653 | 24.6621 | 1800 | 0.5318 | 0.4069 | 0.3592 | 0.2589 | 0.2894 | 0.7910 | 0.5274 | 0.6328 | 0.0499 |
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- | 0.345 | 26.0276 | 1900 | 0.5098 | 0.4069 | 0.4302 | 0.2733 | 0.3123 | 0.8281 | 0.5274 | 0.6444 | 0.0475 |
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- | 0.3157 | 27.4 | 2000 | 0.5230 | 0.4483 | 0.5469 | 0.3472 | 0.3957 | 0.7947 | 0.5970 | 0.6818 | 0.0454 |
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- | 0.2823 | 28.7724 | 2100 | 0.5017 | 0.4069 | 0.4945 | 0.3191 | 0.3652 | 0.8438 | 0.5373 | 0.6565 | 0.0458 |
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- | 0.2878 | 30.1379 | 2200 | 0.5023 | 0.3931 | 0.5167 | 0.3218 | 0.3773 | 0.7786 | 0.5423 | 0.6393 | 0.0499 |
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- | 0.2838 | 31.5103 | 2300 | 0.5143 | 0.3931 | 0.4767 | 0.3381 | 0.3828 | 0.7714 | 0.5373 | 0.6334 | 0.0507 |
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- | 0.2613 | 32.8828 | 2400 | 0.5150 | 0.3931 | 0.5549 | 0.3581 | 0.4093 | 0.7838 | 0.5771 | 0.6648 | 0.0475 |
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- | 0.2392 | 34.2483 | 2500 | 0.4928 | 0.4069 | 0.5825 | 0.4180 | 0.4727 | 0.7718 | 0.5721 | 0.6571 | 0.0487 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [maximuspowers/bert-philosophy-adapted](https://huggingface.co/maximuspowers/bert-philosophy-adapted) on the None dataset.
18
  It achieves the following results on the evaluation set:
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+ - Loss: 0.6556
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+ - Exact Match Accuracy: 0.4258
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+ - Macro Precision: 0.5340
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+ - Macro Recall: 0.4231
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+ - Macro F1: 0.4521
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+ - Micro Precision: 0.7162
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+ - Micro Recall: 0.5372
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+ - Micro F1: 0.6139
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+ - Hamming Loss: 0.0563
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Exact Match Accuracy | Macro Precision | Macro Recall | Macro F1 | Micro Precision | Micro Recall | Micro F1 | Hamming Loss |
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  |:-------------:|:-------:|:----:|:---------------:|:--------------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------:|
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+ | 1.8421 | 0.9569 | 100 | 0.8926 | 0.0 | 0.0392 | 0.0015 | 0.0029 | 0.6667 | 0.0068 | 0.0134 | 0.0830 |
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+ | 1.3499 | 1.9091 | 200 | 0.8203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0833 |
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+ | 1.1328 | 2.8612 | 300 | 0.7838 | 0.0096 | 0.0588 | 0.0015 | 0.0030 | 1.0 | 0.0068 | 0.0134 | 0.0827 |
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+ | 1.0661 | 3.8134 | 400 | 0.7320 | 0.0622 | 0.0588 | 0.0138 | 0.0223 | 1.0 | 0.0608 | 0.1146 | 0.0782 |
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+ | 0.9702 | 4.7656 | 500 | 0.7196 | 0.0813 | 0.0566 | 0.0191 | 0.0286 | 0.9615 | 0.0845 | 0.1553 | 0.0766 |
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+ | 0.9244 | 5.7177 | 600 | 0.7005 | 0.2057 | 0.1051 | 0.0684 | 0.0822 | 0.8684 | 0.2230 | 0.3548 | 0.0675 |
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+ | 0.8352 | 6.6699 | 700 | 0.6485 | 0.1962 | 0.1049 | 0.0700 | 0.0839 | 0.8904 | 0.2196 | 0.3523 | 0.0673 |
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+ | 0.7691 | 7.6220 | 800 | 0.6545 | 0.2488 | 0.2016 | 0.1010 | 0.1283 | 0.8495 | 0.2669 | 0.4062 | 0.0650 |
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+ | 0.7258 | 8.5742 | 900 | 0.6526 | 0.2967 | 0.2067 | 0.1288 | 0.1558 | 0.8509 | 0.3277 | 0.4732 | 0.0608 |
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+ | 0.6766 | 9.5263 | 1000 | 0.6446 | 0.3014 | 0.2044 | 0.1216 | 0.1463 | 0.8319 | 0.3345 | 0.4771 | 0.0611 |
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+ | 0.6739 | 10.4785 | 1100 | 0.6664 | 0.3254 | 0.2547 | 0.1490 | 0.1703 | 0.8 | 0.3649 | 0.5012 | 0.0605 |
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+ | 0.642 | 11.4306 | 1200 | 0.6206 | 0.3445 | 0.2981 | 0.1740 | 0.2035 | 0.7877 | 0.3885 | 0.5204 | 0.0597 |
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+ | 0.5809 | 12.3828 | 1300 | 0.6371 | 0.3254 | 0.2886 | 0.1628 | 0.1938 | 0.7836 | 0.3547 | 0.4884 | 0.0619 |
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+ | 0.5713 | 13.3349 | 1400 | 0.6561 | 0.3397 | 0.2854 | 0.1853 | 0.2142 | 0.7613 | 0.3986 | 0.5233 | 0.0605 |
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+ | 0.5107 | 14.2871 | 1500 | 0.6562 | 0.3493 | 0.2772 | 0.1905 | 0.2153 | 0.7321 | 0.4155 | 0.5302 | 0.0614 |
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+ | 0.4788 | 15.2392 | 1600 | 0.6890 | 0.3636 | 0.3302 | 0.2029 | 0.2246 | 0.7293 | 0.4459 | 0.5535 | 0.0599 |
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+ | 0.4637 | 16.1914 | 1700 | 0.6392 | 0.3301 | 0.3528 | 0.1787 | 0.2149 | 0.8175 | 0.3784 | 0.5173 | 0.0588 |
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+ | 0.4466 | 17.1435 | 1800 | 0.6261 | 0.3636 | 0.3947 | 0.2239 | 0.2578 | 0.7558 | 0.4392 | 0.5556 | 0.0585 |
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+ | 0.4072 | 18.0957 | 1900 | 0.6637 | 0.3589 | 0.4523 | 0.2324 | 0.2800 | 0.7593 | 0.4155 | 0.5371 | 0.0597 |
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+ | 0.399 | 19.0478 | 2000 | 0.6127 | 0.3732 | 0.4496 | 0.2368 | 0.2886 | 0.7764 | 0.4223 | 0.5470 | 0.0583 |
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+ | 0.3888 | 20.0 | 2100 | 0.6061 | 0.3397 | 0.3523 | 0.2316 | 0.2682 | 0.7381 | 0.4189 | 0.5345 | 0.0608 |
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+ | 0.3246 | 20.9569 | 2200 | 0.6353 | 0.3876 | 0.3917 | 0.2404 | 0.2772 | 0.7446 | 0.4628 | 0.5708 | 0.0580 |
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+ | 0.3715 | 21.9091 | 2300 | 0.6690 | 0.3589 | 0.3676 | 0.2448 | 0.2874 | 0.7399 | 0.4324 | 0.5458 | 0.0599 |
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+ | 0.3334 | 22.8612 | 2400 | 0.6332 | 0.3828 | 0.3711 | 0.2705 | 0.2998 | 0.6935 | 0.4662 | 0.5576 | 0.0616 |
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+ | 0.3133 | 23.8134 | 2500 | 0.6497 | 0.4115 | 0.4081 | 0.2998 | 0.3374 | 0.7101 | 0.4966 | 0.5845 | 0.0588 |
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+ | 0.3031 | 24.7656 | 2600 | 0.6581 | 0.4115 | 0.5044 | 0.3476 | 0.3848 | 0.7170 | 0.5135 | 0.5984 | 0.0574 |
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+ | 0.3042 | 25.7177 | 2700 | 0.6811 | 0.3971 | 0.5016 | 0.3270 | 0.3677 | 0.7231 | 0.4764 | 0.5743 | 0.0588 |
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+ | 0.289 | 26.6699 | 2800 | 0.7093 | 0.4163 | 0.5697 | 0.3607 | 0.4106 | 0.7054 | 0.5338 | 0.6077 | 0.0574 |
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+ | 0.265 | 27.6220 | 2900 | 0.6164 | 0.4498 | 0.5669 | 0.4125 | 0.4533 | 0.7373 | 0.5405 | 0.6238 | 0.0543 |
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+ | 0.2171 | 28.5742 | 3000 | 0.6641 | 0.4354 | 0.5524 | 0.4115 | 0.4468 | 0.7093 | 0.5439 | 0.6157 | 0.0566 |
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+ | 0.2337 | 29.5263 | 3100 | 0.6319 | 0.4211 | 0.6194 | 0.4113 | 0.4480 | 0.7085 | 0.5338 | 0.6089 | 0.0571 |
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+ | 0.2122 | 30.4785 | 3200 | 0.6563 | 0.4067 | 0.5731 | 0.3527 | 0.4095 | 0.7170 | 0.5135 | 0.5984 | 0.0574 |
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+ | 0.2117 | 31.4306 | 3300 | 0.6643 | 0.4067 | 0.5312 | 0.4174 | 0.4560 | 0.7184 | 0.5 | 0.5896 | 0.0580 |
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+ | 0.2065 | 32.3828 | 3400 | 0.6556 | 0.4258 | 0.5340 | 0.4231 | 0.4521 | 0.7162 | 0.5372 | 0.6139 | 0.0563 |
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  ### Framework versions
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- "train_steps_per_second": 55.345
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