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maximuspowers
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bert-philosophy-classifier

Text Classification
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bert
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  1. README.md +67 -37
  2. all_results.json +5 -17
  3. runs/Jun28_21-18-35_821879c92305/events.out.tfevents.1751145516.821879c92305.4121.0 +2 -2
  4. train_results.json +5 -5
README.md CHANGED
@@ -7,23 +7,23 @@ tags:
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  model-index:
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  - name: bert-philosophy-classifier
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  results: []
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- datasets:
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- - maximuspowers/philosophy-schools-multilabel
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- pipeline_tag: text-classification
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  ---
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  # bert-philosophy-classifier
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- This model is a fine-tuned version of [maximuspowers/bert-philosophy-adapted](https://huggingface.co/maximuspowers/bert-philosophy-adapted) on a dataset of snippets representing various philosophies, labeled with multi-label school of philosophy labels.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5626
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- - Exact Match Accuracy: 0.3271
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- - Macro Precision: 0.5032
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- - Macro Recall: 0.2266
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- - Macro F1: 0.2665
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- - Micro Precision: 0.7745
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- - Micro Recall: 0.4480
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- - Micro F1: 0.5676
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  - Hamming Loss: 0.0635
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  ## Model description
@@ -57,30 +57,60 @@ The following hyperparameters were used during training:
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  ### Training results
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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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- | 1.8839 | 0.4167 | 100 | 1.0236 | 0.0396 | 0.0315 | 0.0123 | 0.0177 | 0.5 | 0.0504 | 0.0916 | 0.0924 |
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- | 1.402 | 0.8333 | 200 | 0.8696 | 0.0042 | 0.0588 | 0.0016 | 0.0032 | 1.0 | 0.0066 | 0.0132 | 0.0918 |
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- | 1.2522 | 1.25 | 300 | 0.8201 | 0.0479 | 0.0588 | 0.0120 | 0.0200 | 1.0 | 0.0491 | 0.0936 | 0.0879 |
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- | 1.1493 | 1.6667 | 400 | 0.7287 | 0.0854 | 0.0555 | 0.0218 | 0.0313 | 0.9437 | 0.0889 | 0.1624 | 0.0847 |
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- | 1.1566 | 2.0833 | 500 | 0.7392 | 0.0646 | 0.0576 | 0.0156 | 0.0246 | 0.9796 | 0.0637 | 0.1196 | 0.0866 |
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- | 1.0856 | 2.5 | 600 | 0.6814 | 0.1292 | 0.1114 | 0.0368 | 0.0450 | 0.896 | 0.1485 | 0.2548 | 0.0803 |
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- | 1.0468 | 2.9167 | 700 | 0.6753 | 0.1229 | 0.1127 | 0.0365 | 0.0452 | 0.9174 | 0.1472 | 0.2537 | 0.0800 |
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- | 1.043 | 3.3333 | 800 | 0.6417 | 0.1542 | 0.1676 | 0.0483 | 0.0614 | 0.8662 | 0.1804 | 0.2986 | 0.0783 |
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- | 0.9489 | 3.75 | 900 | 0.6827 | 0.1562 | 0.1681 | 0.0513 | 0.0701 | 0.9145 | 0.1844 | 0.3068 | 0.0770 |
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- | 0.9773 | 4.1667 | 1000 | 0.6348 | 0.2188 | 0.1554 | 0.0830 | 0.1048 | 0.8559 | 0.2679 | 0.4081 | 0.0718 |
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- | 0.9368 | 4.5833 | 1100 | 0.6322 | 0.2167 | 0.1525 | 0.0795 | 0.0989 | 0.8277 | 0.2613 | 0.3972 | 0.0733 |
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- | 0.9063 | 5.0 | 1200 | 0.6395 | 0.225 | 0.1502 | 0.0865 | 0.1080 | 0.8487 | 0.2679 | 0.4073 | 0.0721 |
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- | 0.8342 | 5.4167 | 1300 | 0.6009 | 0.2437 | 0.2571 | 0.1040 | 0.1337 | 0.8456 | 0.2905 | 0.4324 | 0.0705 |
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- | 0.8722 | 5.8333 | 1400 | 0.6168 | 0.2437 | 0.1936 | 0.1053 | 0.1321 | 0.8441 | 0.2944 | 0.4366 | 0.0702 |
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- | 0.7927 | 6.25 | 1500 | 0.6005 | 0.2646 | 0.2561 | 0.1262 | 0.1552 | 0.8357 | 0.3170 | 0.4596 | 0.0689 |
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- | 0.8029 | 6.6667 | 1600 | 0.5795 | 0.2479 | 0.3469 | 0.1314 | 0.1761 | 0.8571 | 0.3103 | 0.4557 | 0.0685 |
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- | 0.7886 | 7.0833 | 1700 | 0.5882 | 0.3208 | 0.4122 | 0.1830 | 0.2325 | 0.8273 | 0.3939 | 0.5337 | 0.0636 |
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- | 0.7246 | 7.5 | 1800 | 0.5620 | 0.2917 | 0.3547 | 0.1514 | 0.1923 | 0.8142 | 0.3488 | 0.4884 | 0.0675 |
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- | 0.7283 | 7.9167 | 1900 | 0.6152 | 0.2833 | 0.3992 | 0.1531 | 0.2023 | 0.8383 | 0.3369 | 0.4806 | 0.0673 |
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- | 0.7024 | 8.3333 | 2000 | 0.5610 | 0.3083 | 0.4750 | 0.1731 | 0.2267 | 0.8369 | 0.3674 | 0.5106 | 0.0651 |
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- | 0.6504 | 8.75 | 2100 | 0.5670 | 0.3125 | 0.5516 | 0.2099 | 0.2723 | 0.7947 | 0.3952 | 0.5279 | 0.0653 |
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- | 0.6453 | 9.1667 | 2200 | 0.5828 | 0.3125 | 0.4952 | 0.2084 | 0.2634 | 0.7850 | 0.4019 | 0.5316 | 0.0654 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
@@ -88,4 +118,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.52.4
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  - Pytorch 2.6.0+cu124
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  - Datasets 3.6.0
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- - Tokenizers 0.21.2
 
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  model-index:
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  - name: bert-philosophy-classifier
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  results: []
 
 
 
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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  # bert-philosophy-classifier
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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: 0.5491
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+ - Exact Match Accuracy: 0.36
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+ - Macro Precision: 0.5561
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+ - Macro Recall: 0.4035
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+ - Macro F1: 0.4602
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+ - Micro Precision: 0.7328
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+ - Micro Recall: 0.5586
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+ - Micro F1: 0.6340
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  - Hamming Loss: 0.0635
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  ## Model description
 
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  ### Training results
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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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  ### Framework versions
 
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  - Transformers 4.52.4
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  - Pytorch 2.6.0+cu124
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  - Datasets 3.6.0
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+ - Tokenizers 0.21.2
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