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bert-philosophy-classifier

Text Classification
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bert
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  1. README.md +43 -61
  2. all_results.json +5 -17
  3. runs/Jun29_19-02-57_45ccd8ae5b94/events.out.tfevents.1751223779.45ccd8ae5b94.1692.0 +2 -2
  4. 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: 0.6170
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- - Exact Match Accuracy: 0.3289
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- - Macro Precision: 0.5583
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- - Macro Recall: 0.3863
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- - Macro F1: 0.4454
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- - Micro Precision: 0.6898
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- - Micro Recall: 0.5159
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- - Micro F1: 0.5903
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- - Hamming Loss: 0.0680
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  ## Model description
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@@ -59,58 +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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- | 1.9157 | 0.3802 | 100 | 0.9992 | 0.0 | 0.0407 | 0.0026 | 0.0048 | 0.6923 | 0.0102 | 0.0202 | 0.0979 |
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- | 1.433 | 0.7605 | 200 | 0.8577 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0985 |
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- | 1.261 | 1.1407 | 300 | 0.8821 | 0.0 | 0.0588 | 0.0003 | 0.0006 | 1.0 | 0.0011 | 0.0023 | 0.0984 |
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- | 1.1991 | 1.5209 | 400 | 0.7624 | 0.0286 | 0.0588 | 0.0122 | 0.0202 | 1.0 | 0.0489 | 0.0933 | 0.0937 |
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- | 1.1809 | 1.9011 | 500 | 0.7328 | 0.0457 | 0.0588 | 0.0176 | 0.0271 | 1.0 | 0.0705 | 0.1318 | 0.0915 |
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- | 1.0938 | 2.2814 | 600 | 0.7042 | 0.0629 | 0.0575 | 0.0256 | 0.0354 | 0.9783 | 0.1024 | 0.1854 | 0.0886 |
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- | 1.1008 | 2.6616 | 700 | 0.6941 | 0.08 | 0.0553 | 0.0313 | 0.0399 | 0.9402 | 0.1251 | 0.2209 | 0.0869 |
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- | 1.0646 | 3.0418 | 800 | 0.6270 | 0.08 | 0.0552 | 0.0344 | 0.0424 | 0.9380 | 0.1377 | 0.2401 | 0.0858 |
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- | 1.0233 | 3.4221 | 900 | 0.6455 | 0.1010 | 0.1701 | 0.0427 | 0.0537 | 0.8994 | 0.1627 | 0.2755 | 0.0843 |
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- | 0.97 | 3.8023 | 1000 | 0.6775 | 0.1181 | 0.1630 | 0.0501 | 0.0699 | 0.9264 | 0.1718 | 0.2898 | 0.0829 |
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- | 0.9737 | 4.1825 | 1100 | 0.6337 | 0.1410 | 0.1515 | 0.0640 | 0.0831 | 0.8597 | 0.2162 | 0.3455 | 0.0807 |
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- | 0.9564 | 4.5627 | 1200 | 0.6215 | 0.2057 | 0.1456 | 0.0930 | 0.1109 | 0.8289 | 0.2867 | 0.4260 | 0.0761 |
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- | 0.9356 | 4.9430 | 1300 | 0.5945 | 0.1886 | 0.1495 | 0.0851 | 0.1053 | 0.8453 | 0.2673 | 0.4062 | 0.0770 |
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- | 0.867 | 5.3232 | 1400 | 0.5871 | 0.2019 | 0.2662 | 0.0950 | 0.1202 | 0.8378 | 0.2821 | 0.4221 | 0.0761 |
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- | 0.9107 | 5.7034 | 1500 | 0.5976 | 0.1943 | 0.3013 | 0.1035 | 0.1374 | 0.8282 | 0.2742 | 0.4120 | 0.0771 |
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- | 0.8554 | 6.0837 | 1600 | 0.5966 | 0.2133 | 0.2838 | 0.1189 | 0.1509 | 0.8110 | 0.3026 | 0.4408 | 0.0756 |
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- | 0.8247 | 6.4639 | 1700 | 0.5772 | 0.2210 | 0.3270 | 0.1291 | 0.1702 | 0.8491 | 0.3072 | 0.4511 | 0.0736 |
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- | 0.8203 | 6.8441 | 1800 | 0.5701 | 0.2648 | 0.5266 | 0.1741 | 0.2244 | 0.8232 | 0.3709 | 0.5114 | 0.0698 |
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- | 0.727 | 7.2243 | 1900 | 0.5601 | 0.2267 | 0.5093 | 0.1544 | 0.2102 | 0.8450 | 0.3288 | 0.4734 | 0.0720 |
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- | 0.7541 | 7.6046 | 2000 | 0.5609 | 0.2724 | 0.4593 | 0.1824 | 0.2396 | 0.8209 | 0.3754 | 0.5152 | 0.0696 |
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- | 0.781 | 7.9848 | 2100 | 0.5527 | 0.2914 | 0.5056 | 0.2097 | 0.2700 | 0.8286 | 0.3959 | 0.5358 | 0.0676 |
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- | 0.7179 | 8.3650 | 2200 | 0.5396 | 0.2705 | 0.5272 | 0.1860 | 0.2531 | 0.84 | 0.3584 | 0.5024 | 0.0699 |
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- | 0.7131 | 8.7452 | 2300 | 0.5501 | 0.2743 | 0.5552 | 0.2042 | 0.2735 | 0.8363 | 0.3720 | 0.5150 | 0.0690 |
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- | 0.6664 | 9.1255 | 2400 | 0.5685 | 0.2933 | 0.4603 | 0.2233 | 0.2804 | 0.7983 | 0.4187 | 0.5493 | 0.0677 |
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- | 0.6507 | 9.5057 | 2500 | 0.5638 | 0.3314 | 0.5954 | 0.2558 | 0.3285 | 0.8168 | 0.4414 | 0.5731 | 0.0648 |
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- | 0.6684 | 9.8859 | 2600 | 0.5676 | 0.2705 | 0.6380 | 0.2202 | 0.3027 | 0.8686 | 0.3686 | 0.5176 | 0.0677 |
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- | 0.604 | 10.2662 | 2700 | 0.6087 | 0.2952 | 0.5374 | 0.2783 | 0.3511 | 0.7883 | 0.4278 | 0.5546 | 0.0677 |
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- | 0.625 | 10.6464 | 2800 | 0.5938 | 0.3105 | 0.5671 | 0.2689 | 0.3422 | 0.7832 | 0.4357 | 0.5599 | 0.0675 |
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- | 0.5915 | 11.0266 | 2900 | 0.5653 | 0.3124 | 0.5755 | 0.2821 | 0.3563 | 0.7928 | 0.4528 | 0.5764 | 0.0655 |
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- | 0.5599 | 11.4068 | 3000 | 0.5655 | 0.3181 | 0.5906 | 0.2902 | 0.3593 | 0.7846 | 0.4642 | 0.5833 | 0.0653 |
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- | 0.5457 | 11.7871 | 3100 | 0.5650 | 0.3124 | 0.6100 | 0.3058 | 0.3743 | 0.7759 | 0.4608 | 0.5782 | 0.0662 |
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- | 0.5365 | 12.1673 | 3200 | 0.5637 | 0.3067 | 0.6418 | 0.2921 | 0.3812 | 0.8063 | 0.4403 | 0.5695 | 0.0655 |
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- | 0.5288 | 12.5475 | 3300 | 0.5512 | 0.3295 | 0.6104 | 0.3281 | 0.3993 | 0.7782 | 0.4869 | 0.5990 | 0.0642 |
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- | 0.5438 | 12.9278 | 3400 | 0.5668 | 0.3143 | 0.6077 | 0.3221 | 0.3992 | 0.7835 | 0.4653 | 0.5839 | 0.0653 |
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- | 0.513 | 13.3080 | 3500 | 0.5715 | 0.3276 | 0.5619 | 0.3553 | 0.4167 | 0.7576 | 0.5085 | 0.6086 | 0.0644 |
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- | 0.4844 | 13.6882 | 3600 | 0.5955 | 0.3524 | 0.5864 | 0.3479 | 0.4261 | 0.7730 | 0.5154 | 0.6184 | 0.0626 |
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- | 0.5115 | 14.0684 | 3700 | 0.5497 | 0.3562 | 0.5769 | 0.3415 | 0.4184 | 0.7815 | 0.5006 | 0.6103 | 0.0630 |
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- | 0.4524 | 14.4487 | 3800 | 0.5876 | 0.3505 | 0.5814 | 0.3511 | 0.4279 | 0.7678 | 0.5040 | 0.6085 | 0.0639 |
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- | 0.4378 | 14.8289 | 3900 | 0.5833 | 0.3276 | 0.5747 | 0.3345 | 0.4129 | 0.7623 | 0.4778 | 0.5874 | 0.0661 |
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- | 0.4541 | 15.2091 | 4000 | 0.5292 | 0.3581 | 0.5828 | 0.3727 | 0.4432 | 0.7544 | 0.5381 | 0.6282 | 0.0627 |
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- | 0.423 | 15.5894 | 4100 | 0.5555 | 0.3562 | 0.5856 | 0.3647 | 0.4383 | 0.7657 | 0.5279 | 0.6249 | 0.0624 |
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- | 0.4367 | 15.9696 | 4200 | 0.5670 | 0.3467 | 0.6071 | 0.3490 | 0.4265 | 0.7792 | 0.5017 | 0.6104 | 0.0631 |
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- | 0.3713 | 16.3498 | 4300 | 0.5770 | 0.3390 | 0.5605 | 0.3637 | 0.4298 | 0.7541 | 0.5233 | 0.6179 | 0.0638 |
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- | 0.3835 | 16.7300 | 4400 | 0.5754 | 0.3543 | 0.5643 | 0.3716 | 0.4364 | 0.7492 | 0.5370 | 0.6256 | 0.0633 |
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- | 0.3723 | 17.1103 | 4500 | 0.5839 | 0.3524 | 0.5978 | 0.3969 | 0.4550 | 0.7438 | 0.5484 | 0.6313 | 0.0631 |
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- | 0.3565 | 17.4905 | 4600 | 0.5713 | 0.3505 | 0.5965 | 0.3854 | 0.4523 | 0.7516 | 0.5301 | 0.6217 | 0.0635 |
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- | 0.3917 | 17.8707 | 4700 | 0.5352 | 0.36 | 0.6236 | 0.3837 | 0.4545 | 0.7619 | 0.5461 | 0.6362 | 0.0615 |
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- | 0.3546 | 18.2510 | 4800 | 0.5972 | 0.32 | 0.5358 | 0.3825 | 0.4392 | 0.7293 | 0.5301 | 0.6140 | 0.0657 |
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- | 0.3363 | 18.6312 | 4900 | 0.5696 | 0.3371 | 0.6057 | 0.3643 | 0.4334 | 0.7414 | 0.5154 | 0.6081 | 0.0654 |
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- | 0.3344 | 19.0114 | 5000 | 0.5925 | 0.3029 | 0.6128 | 0.3819 | 0.4548 | 0.7241 | 0.4926 | 0.5863 | 0.0685 |
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- | 0.3148 | 19.3916 | 5100 | 0.5891 | 0.3429 | 0.5458 | 0.4026 | 0.4575 | 0.7303 | 0.5484 | 0.6264 | 0.0644 |
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- | 0.3474 | 19.7719 | 5200 | 0.5491 | 0.36 | 0.5561 | 0.4035 | 0.4602 | 0.7328 | 0.5586 | 0.6340 | 0.0635 |
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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:
19
+ - Loss: 0.5517
20
+ - Exact Match Accuracy: 0.3175
21
+ - Macro Precision: 0.6677
22
+ - Macro Recall: 0.3737
23
+ - Macro F1: 0.4510
24
+ - Micro Precision: 0.7676
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+ - Micro Recall: 0.4542
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+ - Micro F1: 0.5707
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+ - Hamming Loss: 0.0665
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  ## Model description
30
 
 
59
 
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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 |
61
  |:-------------:|:-------:|:----:|:---------------:|:--------------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------:|
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+ | 1.9545 | 0.3521 | 100 | 1.0206 | 0.0071 | 0.0171 | 0.0027 | 0.0047 | 0.25 | 0.0096 | 0.0185 | 0.0992 |
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+ | 1.4947 | 0.7042 | 200 | 0.9205 | 0.0 | 0.0588 | 0.0003 | 0.0006 | 1.0 | 0.0011 | 0.0021 | 0.0972 |
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+ | 1.2688 | 1.0563 | 300 | 0.8579 | 0.0 | 0.0588 | 0.0003 | 0.0006 | 1.0 | 0.0011 | 0.0021 | 0.0972 |
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+ | 1.2271 | 1.4085 | 400 | 0.9072 | 0.0071 | 0.0588 | 0.0030 | 0.0058 | 1.0 | 0.0107 | 0.0211 | 0.0963 |
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+ | 1.1877 | 1.7606 | 500 | 0.7930 | 0.0353 | 0.0551 | 0.0136 | 0.0219 | 0.9375 | 0.0480 | 0.0913 | 0.0930 |
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+ | 1.1545 | 2.1127 | 600 | 0.7768 | 0.0670 | 0.0537 | 0.0255 | 0.0346 | 0.9130 | 0.0896 | 0.1631 | 0.0894 |
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+ | 1.1276 | 2.4648 | 700 | 0.7173 | 0.0864 | 0.0521 | 0.0303 | 0.0383 | 0.8850 | 0.1066 | 0.1903 | 0.0883 |
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+ | 1.1083 | 2.8169 | 800 | 0.7093 | 0.0758 | 0.1126 | 0.0298 | 0.0394 | 0.9143 | 0.1023 | 0.1841 | 0.0883 |
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+ | 1.0268 | 3.1690 | 900 | 0.6733 | 0.1041 | 0.1640 | 0.0517 | 0.0644 | 0.8057 | 0.1503 | 0.2534 | 0.0862 |
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+ | 1.0161 | 3.5211 | 1000 | 0.6472 | 0.1164 | 0.1559 | 0.0634 | 0.0861 | 0.8533 | 0.1674 | 0.2799 | 0.0838 |
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+ | 0.9917 | 3.8732 | 1100 | 0.7055 | 0.1358 | 0.2132 | 0.0736 | 0.0970 | 0.8465 | 0.1940 | 0.3157 | 0.0819 |
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+ | 0.9533 | 4.2254 | 1200 | 0.6556 | 0.1834 | 0.2694 | 0.1242 | 0.1646 | 0.8812 | 0.2452 | 0.3837 | 0.0767 |
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+ | 0.9747 | 4.5775 | 1300 | 0.6144 | 0.2011 | 0.2716 | 0.1285 | 0.1690 | 0.8773 | 0.2591 | 0.4 | 0.0756 |
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+ | 0.9275 | 4.9296 | 1400 | 0.6027 | 0.2063 | 0.2682 | 0.1408 | 0.1804 | 0.8513 | 0.2868 | 0.4290 | 0.0743 |
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+ | 0.8702 | 5.2817 | 1500 | 0.6040 | 0.2240 | 0.3197 | 0.1559 | 0.1977 | 0.8542 | 0.3060 | 0.4505 | 0.0726 |
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+ | 0.8582 | 5.6338 | 1600 | 0.6104 | 0.2293 | 0.3684 | 0.1697 | 0.2177 | 0.8426 | 0.3081 | 0.4512 | 0.0729 |
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+ | 0.8783 | 5.9859 | 1700 | 0.5885 | 0.2328 | 0.3749 | 0.1646 | 0.2117 | 0.8657 | 0.3092 | 0.4556 | 0.0719 |
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+ | 0.8147 | 6.3380 | 1800 | 0.5681 | 0.2469 | 0.4728 | 0.1941 | 0.2427 | 0.8215 | 0.3337 | 0.4746 | 0.0719 |
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+ | 0.8155 | 6.6901 | 1900 | 0.5858 | 0.2399 | 0.3577 | 0.1873 | 0.2337 | 0.8144 | 0.3369 | 0.4766 | 0.0720 |
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+ | 0.812 | 7.0423 | 2000 | 0.5932 | 0.2434 | 0.5377 | 0.2240 | 0.2870 | 0.8285 | 0.3348 | 0.4768 | 0.0715 |
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+ | 0.7735 | 7.3944 | 2100 | 0.5969 | 0.2504 | 0.4537 | 0.2217 | 0.2802 | 0.7844 | 0.3529 | 0.4868 | 0.0724 |
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+ | 0.7747 | 7.7465 | 2200 | 0.5980 | 0.2734 | 0.5684 | 0.2460 | 0.3142 | 0.7941 | 0.3699 | 0.5047 | 0.0707 |
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+ | 0.6935 | 8.0986 | 2300 | 0.5834 | 0.2822 | 0.4822 | 0.2493 | 0.3069 | 0.7669 | 0.3859 | 0.5135 | 0.0712 |
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+ | 0.7359 | 8.4507 | 2400 | 0.5643 | 0.2875 | 0.5755 | 0.2854 | 0.3535 | 0.7991 | 0.3987 | 0.5320 | 0.0683 |
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+ | 0.6547 | 8.8028 | 2500 | 0.5672 | 0.2875 | 0.5700 | 0.2989 | 0.3656 | 0.7878 | 0.4115 | 0.5406 | 0.0681 |
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+ | 0.6568 | 9.1549 | 2600 | 0.5804 | 0.2857 | 0.5921 | 0.2826 | 0.3611 | 0.8174 | 0.3913 | 0.5292 | 0.0677 |
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+ | 0.683 | 9.5070 | 2700 | 0.5911 | 0.2787 | 0.5610 | 0.2682 | 0.3399 | 0.7577 | 0.3934 | 0.5179 | 0.0713 |
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+ | 0.6916 | 9.8592 | 2800 | 0.5553 | 0.2892 | 0.6354 | 0.3208 | 0.3899 | 0.7882 | 0.4126 | 0.5416 | 0.0680 |
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+ | 0.6112 | 10.2113 | 2900 | 0.5829 | 0.3228 | 0.6405 | 0.3521 | 0.4351 | 0.7911 | 0.4563 | 0.5788 | 0.0646 |
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+ | 0.6032 | 10.5634 | 3000 | 0.6113 | 0.3069 | 0.6247 | 0.3173 | 0.3949 | 0.7556 | 0.4350 | 0.5521 | 0.0687 |
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+ | 0.5927 | 10.9155 | 3100 | 0.5666 | 0.3016 | 0.6423 | 0.3289 | 0.4154 | 0.8065 | 0.4222 | 0.5542 | 0.0661 |
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+ | 0.5639 | 11.2676 | 3200 | 0.5527 | 0.3086 | 0.5956 | 0.3482 | 0.4169 | 0.7522 | 0.4563 | 0.5680 | 0.0675 |
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+ | 0.5965 | 11.6197 | 3300 | 0.5370 | 0.3192 | 0.6174 | 0.3337 | 0.4061 | 0.7692 | 0.4584 | 0.5745 | 0.0661 |
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+ | 0.5809 | 11.9718 | 3400 | 0.5517 | 0.3175 | 0.6677 | 0.3737 | 0.4510 | 0.7676 | 0.4542 | 0.5707 | 0.0665 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  }
 
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