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---
library_name: transformers
license: apache-2.0
base_model: maximuspowers/bert-philosophy-adapted
tags:
- generated_from_trainer
model-index:
- name: bert-philosophy-classifier
results: []
datasets:
- maximuspowers/philpapers-papers-summarized-labeled
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-philosophy-classifier
This model is a fine-tuned version of [maximuspowers/bert-philosophy-adapted](https://huggingface.co/maximuspowers/bert-philosophy-adapted) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5565
- Exact Match Accuracy: 0.2430
- Macro Precision: 0.5046
- Macro Recall: 0.2169
- Macro F1: 0.2688
- Micro Precision: 0.8130
- Micro Recall: 0.3380
- Micro F1: 0.4775
- Hamming Loss: 0.0709
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match Accuracy | Macro Precision | Macro Recall | Macro F1 | Micro Precision | Micro Recall | Micro F1 | Hamming Loss |
|:-------------:|:-------:|:----:|:---------------:|:--------------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------:|
| 1.9545 | 0.3521 | 100 | 1.0206 | 0.0071 | 0.0171 | 0.0027 | 0.0047 | 0.25 | 0.0096 | 0.0185 | 0.0992 |
| 1.4947 | 0.7042 | 200 | 0.9205 | 0.0 | 0.0588 | 0.0003 | 0.0006 | 1.0 | 0.0011 | 0.0021 | 0.0972 |
| 1.2688 | 1.0563 | 300 | 0.8579 | 0.0 | 0.0588 | 0.0003 | 0.0006 | 1.0 | 0.0011 | 0.0021 | 0.0972 |
| 1.2271 | 1.4085 | 400 | 0.9072 | 0.0071 | 0.0588 | 0.0030 | 0.0058 | 1.0 | 0.0107 | 0.0211 | 0.0963 |
| 1.1877 | 1.7606 | 500 | 0.7930 | 0.0353 | 0.0551 | 0.0136 | 0.0219 | 0.9375 | 0.0480 | 0.0913 | 0.0930 |
| 1.1545 | 2.1127 | 600 | 0.7768 | 0.0670 | 0.0537 | 0.0255 | 0.0346 | 0.9130 | 0.0896 | 0.1631 | 0.0894 |
| 1.1276 | 2.4648 | 700 | 0.7173 | 0.0864 | 0.0521 | 0.0303 | 0.0383 | 0.8850 | 0.1066 | 0.1903 | 0.0883 |
| 1.1083 | 2.8169 | 800 | 0.7093 | 0.0758 | 0.1126 | 0.0298 | 0.0394 | 0.9143 | 0.1023 | 0.1841 | 0.0883 |
| 1.0268 | 3.1690 | 900 | 0.6733 | 0.1041 | 0.1640 | 0.0517 | 0.0644 | 0.8057 | 0.1503 | 0.2534 | 0.0862 |
| 1.0161 | 3.5211 | 1000 | 0.6472 | 0.1164 | 0.1559 | 0.0634 | 0.0861 | 0.8533 | 0.1674 | 0.2799 | 0.0838 |
| 0.9917 | 3.8732 | 1100 | 0.7055 | 0.1358 | 0.2132 | 0.0736 | 0.0970 | 0.8465 | 0.1940 | 0.3157 | 0.0819 |
| 0.9533 | 4.2254 | 1200 | 0.6556 | 0.1834 | 0.2694 | 0.1242 | 0.1646 | 0.8812 | 0.2452 | 0.3837 | 0.0767 |
| 0.9747 | 4.5775 | 1300 | 0.6144 | 0.2011 | 0.2716 | 0.1285 | 0.1690 | 0.8773 | 0.2591 | 0.4 | 0.0756 |
| 0.9275 | 4.9296 | 1400 | 0.6027 | 0.2063 | 0.2682 | 0.1408 | 0.1804 | 0.8513 | 0.2868 | 0.4290 | 0.0743 |
| 0.8702 | 5.2817 | 1500 | 0.6040 | 0.2240 | 0.3197 | 0.1559 | 0.1977 | 0.8542 | 0.3060 | 0.4505 | 0.0726 |
| 0.8582 | 5.6338 | 1600 | 0.6104 | 0.2293 | 0.3684 | 0.1697 | 0.2177 | 0.8426 | 0.3081 | 0.4512 | 0.0729 |
| 0.8783 | 5.9859 | 1700 | 0.5885 | 0.2328 | 0.3749 | 0.1646 | 0.2117 | 0.8657 | 0.3092 | 0.4556 | 0.0719 |
| 0.8147 | 6.3380 | 1800 | 0.5681 | 0.2469 | 0.4728 | 0.1941 | 0.2427 | 0.8215 | 0.3337 | 0.4746 | 0.0719 |
| 0.8155 | 6.6901 | 1900 | 0.5858 | 0.2399 | 0.3577 | 0.1873 | 0.2337 | 0.8144 | 0.3369 | 0.4766 | 0.0720 |
| 0.812 | 7.0423 | 2000 | 0.5932 | 0.2434 | 0.5377 | 0.2240 | 0.2870 | 0.8285 | 0.3348 | 0.4768 | 0.0715 |
| 0.7735 | 7.3944 | 2100 | 0.5969 | 0.2504 | 0.4537 | 0.2217 | 0.2802 | 0.7844 | 0.3529 | 0.4868 | 0.0724 |
| 0.7747 | 7.7465 | 2200 | 0.5980 | 0.2734 | 0.5684 | 0.2460 | 0.3142 | 0.7941 | 0.3699 | 0.5047 | 0.0707 |
| 0.6935 | 8.0986 | 2300 | 0.5834 | 0.2822 | 0.4822 | 0.2493 | 0.3069 | 0.7669 | 0.3859 | 0.5135 | 0.0712 |
| 0.7359 | 8.4507 | 2400 | 0.5643 | 0.2875 | 0.5755 | 0.2854 | 0.3535 | 0.7991 | 0.3987 | 0.5320 | 0.0683 |
| 0.6547 | 8.8028 | 2500 | 0.5672 | 0.2875 | 0.5700 | 0.2989 | 0.3656 | 0.7878 | 0.4115 | 0.5406 | 0.0681 |
| 0.6568 | 9.1549 | 2600 | 0.5804 | 0.2857 | 0.5921 | 0.2826 | 0.3611 | 0.8174 | 0.3913 | 0.5292 | 0.0677 |
| 0.683 | 9.5070 | 2700 | 0.5911 | 0.2787 | 0.5610 | 0.2682 | 0.3399 | 0.7577 | 0.3934 | 0.5179 | 0.0713 |
| 0.6916 | 9.8592 | 2800 | 0.5553 | 0.2892 | 0.6354 | 0.3208 | 0.3899 | 0.7882 | 0.4126 | 0.5416 | 0.0680 |
| 0.6112 | 10.2113 | 2900 | 0.5829 | 0.3228 | 0.6405 | 0.3521 | 0.4351 | 0.7911 | 0.4563 | 0.5788 | 0.0646 |
| 0.6032 | 10.5634 | 3000 | 0.6113 | 0.3069 | 0.6247 | 0.3173 | 0.3949 | 0.7556 | 0.4350 | 0.5521 | 0.0687 |
| 0.5927 | 10.9155 | 3100 | 0.5666 | 0.3016 | 0.6423 | 0.3289 | 0.4154 | 0.8065 | 0.4222 | 0.5542 | 0.0661 |
| 0.5639 | 11.2676 | 3200 | 0.5527 | 0.3086 | 0.5956 | 0.3482 | 0.4169 | 0.7522 | 0.4563 | 0.5680 | 0.0675 |
| 0.5965 | 11.6197 | 3300 | 0.5370 | 0.3192 | 0.6174 | 0.3337 | 0.4061 | 0.7692 | 0.4584 | 0.5745 | 0.0661 |
| 0.5809 | 11.9718 | 3400 | 0.5517 | 0.3175 | 0.6677 | 0.3737 | 0.4510 | 0.7676 | 0.4542 | 0.5707 | 0.0665 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2 |