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---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: modernbert-base-multi-head-values-context
results: []
---
<!-- 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. -->
# modernbert-base-multi-head-values-context
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2990
- Subset Accuracy: 0.2753
- F1 Macro: 0.3032
- F1 Micro: 0.3876
- Precision Macro: 0.4109
- Recall Macro: 0.2499
- Roc Auc: 0.7910
## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 2025
- gradient_accumulation_steps: 8
- 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_ratio: 0.01
- num_epochs: 33
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Subset Accuracy | F1 Macro | F1 Micro | Precision Macro | Recall Macro | Roc Auc |
|:-------------:|:-------:|:-----:|:---------------:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------:|
| 2.5451 | 0.5002 | 767 | 0.2012 | 0.0027 | 0.0023 | 0.0050 | 0.0718 | 0.0012 | 0.6531 |
| 1.5075 | 1.0 | 1534 | 0.1838 | 0.0768 | 0.0569 | 0.1319 | 0.2330 | 0.0353 | 0.7437 |
| 1.4382 | 1.5002 | 2301 | 0.1781 | 0.1437 | 0.1318 | 0.2281 | 0.3534 | 0.0891 | 0.7792 |
| 1.3858 | 2.0 | 3068 | 0.1710 | 0.1680 | 0.1582 | 0.2615 | 0.4338 | 0.1091 | 0.7962 |
| 1.3157 | 2.5002 | 3835 | 0.1681 | 0.1822 | 0.1787 | 0.2796 | 0.4967 | 0.1267 | 0.8058 |
| 1.291 | 3.0 | 4602 | 0.1622 | 0.2229 | 0.2115 | 0.3291 | 0.6302 | 0.1523 | 0.8195 |
| 1.2388 | 3.5002 | 5369 | 0.1614 | 0.2026 | 0.2201 | 0.3082 | 0.6143 | 0.1536 | 0.8222 |
| 1.1993 | 4.0 | 6136 | 0.1583 | 0.2445 | 0.2454 | 0.3554 | 0.5956 | 0.1783 | 0.8291 |
| 1.1415 | 4.5002 | 6903 | 0.1608 | 0.2793 | 0.2883 | 0.3934 | 0.5614 | 0.2220 | 0.8288 |
| 1.1221 | 5.0 | 7670 | 0.1595 | 0.2384 | 0.2523 | 0.3533 | 0.5982 | 0.1761 | 0.8342 |
| 1.0726 | 5.5002 | 8437 | 0.1604 | 0.2727 | 0.2930 | 0.3906 | 0.5584 | 0.2178 | 0.8318 |
| 1.0381 | 6.0 | 9204 | 0.1629 | 0.2599 | 0.2693 | 0.3759 | 0.5421 | 0.2099 | 0.8315 |
| 0.9957 | 6.5002 | 9971 | 0.1662 | 0.2814 | 0.2856 | 0.4001 | 0.5380 | 0.2223 | 0.8300 |
| 0.9319 | 7.0 | 10738 | 0.1640 | 0.2604 | 0.2960 | 0.3820 | 0.5431 | 0.2201 | 0.8288 |
| 0.8279 | 7.5002 | 11505 | 0.1733 | 0.2788 | 0.2953 | 0.3939 | 0.5275 | 0.2307 | 0.8245 |
| 0.8365 | 8.0 | 12272 | 0.1742 | 0.2757 | 0.3004 | 0.3910 | 0.5030 | 0.2339 | 0.8218 |
| 0.7168 | 8.5002 | 13039 | 0.1810 | 0.2863 | 0.3063 | 0.4020 | 0.4589 | 0.2499 | 0.8202 |
| 0.7158 | 9.0 | 13806 | 0.1804 | 0.2758 | 0.3052 | 0.3910 | 0.4622 | 0.2392 | 0.8212 |
| 0.5827 | 9.5002 | 14573 | 0.1880 | 0.2878 | 0.3166 | 0.4034 | 0.4568 | 0.2584 | 0.8159 |
| 0.5958 | 10.0 | 15340 | 0.1906 | 0.2788 | 0.3114 | 0.3940 | 0.4912 | 0.2522 | 0.8134 |
| 0.4641 | 10.5002 | 16107 | 0.1978 | 0.2750 | 0.3104 | 0.3896 | 0.4505 | 0.2501 | 0.8106 |
| 0.4608 | 11.0 | 16874 | 0.2022 | 0.2724 | 0.3026 | 0.3880 | 0.4840 | 0.2470 | 0.8082 |
| 0.3546 | 11.5002 | 17641 | 0.2113 | 0.2773 | 0.3120 | 0.3922 | 0.4598 | 0.2556 | 0.8038 |
| 0.3575 | 12.0 | 18408 | 0.2133 | 0.2834 | 0.3092 | 0.3980 | 0.4361 | 0.2535 | 0.8045 |
| 0.2601 | 12.5002 | 19175 | 0.2226 | 0.2778 | 0.3104 | 0.3897 | 0.4274 | 0.2559 | 0.8003 |
| 0.258 | 13.0 | 19942 | 0.2275 | 0.2824 | 0.3176 | 0.3956 | 0.4188 | 0.2643 | 0.8003 |
| 0.1778 | 13.5002 | 20709 | 0.2375 | 0.2686 | 0.3035 | 0.3815 | 0.4103 | 0.2496 | 0.7994 |
| 0.1803 | 14.0 | 21476 | 0.2426 | 0.2713 | 0.3083 | 0.3865 | 0.4305 | 0.2522 | 0.7968 |
| 0.1233 | 14.5002 | 22243 | 0.2501 | 0.2781 | 0.3139 | 0.3906 | 0.4473 | 0.2592 | 0.7970 |
| 0.1197 | 15.0 | 23010 | 0.2566 | 0.2735 | 0.3081 | 0.3864 | 0.4231 | 0.2519 | 0.7950 |
| 0.0804 | 15.5002 | 23777 | 0.2653 | 0.2746 | 0.3065 | 0.3839 | 0.4267 | 0.2512 | 0.7941 |
| 0.0813 | 16.0 | 24544 | 0.2723 | 0.2740 | 0.3078 | 0.3861 | 0.4372 | 0.2505 | 0.7931 |
| 0.0548 | 16.5002 | 25311 | 0.2813 | 0.2776 | 0.3077 | 0.3922 | 0.4544 | 0.2500 | 0.7927 |
| 0.0535 | 17.0 | 26078 | 0.2882 | 0.2804 | 0.3093 | 0.3912 | 0.4497 | 0.2528 | 0.7914 |
| 0.0387 | 17.5002 | 26845 | 0.2990 | 0.2753 | 0.3032 | 0.3876 | 0.4109 | 0.2499 | 0.7910 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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