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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-roc_auc
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-roc_auc
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: 0.1835
- Subset Accuracy: 0.3032
- F1 Macro: 0.3352
- F1 Micro: 0.4194
- Precision Macro: 0.4748
- Recall Macro: 0.2755
- Roc Auc: 0.8264
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------:|
| 2.6266 | 0.5002 | 767 | 0.2033 | 0.0069 | 0.0054 | 0.0125 | 0.0347 | 0.0029 | 0.6535 |
| 1.5099 | 1.0 | 1534 | 0.1838 | 0.0815 | 0.0668 | 0.1394 | 0.2799 | 0.0412 | 0.7493 |
| 1.4376 | 1.5002 | 2301 | 0.1765 | 0.1326 | 0.1239 | 0.2181 | 0.3987 | 0.0827 | 0.7851 |
| 1.3726 | 2.0 | 3068 | 0.1680 | 0.1946 | 0.1786 | 0.2948 | 0.4573 | 0.1265 | 0.8058 |
| 1.2975 | 2.5002 | 3835 | 0.1653 | 0.2098 | 0.1955 | 0.3157 | 0.4924 | 0.1455 | 0.8188 |
| 1.2668 | 3.0 | 4602 | 0.1598 | 0.2547 | 0.2460 | 0.3660 | 0.5225 | 0.1833 | 0.8312 |
| 1.2172 | 3.5002 | 5369 | 0.1579 | 0.2364 | 0.2546 | 0.3487 | 0.5852 | 0.1808 | 0.8361 |
| 1.1801 | 4.0 | 6136 | 0.1554 | 0.2426 | 0.2481 | 0.3576 | 0.6564 | 0.1772 | 0.8417 |
| 1.1193 | 4.5002 | 6903 | 0.1562 | 0.2773 | 0.2924 | 0.3950 | 0.5843 | 0.2169 | 0.8420 |
| 1.1036 | 5.0 | 7670 | 0.1555 | 0.2656 | 0.2821 | 0.3803 | 0.5656 | 0.2047 | 0.8451 |
| 1.0424 | 5.5002 | 8437 | 0.1578 | 0.3004 | 0.3118 | 0.4169 | 0.5477 | 0.2396 | 0.8442 |
| 1.0105 | 6.0 | 9204 | 0.1586 | 0.2747 | 0.3026 | 0.3951 | 0.5986 | 0.2289 | 0.8436 |
| 0.9595 | 6.5002 | 9971 | 0.1628 | 0.3067 | 0.3102 | 0.4204 | 0.5569 | 0.2437 | 0.8399 |
| 0.9071 | 7.0 | 10738 | 0.1627 | 0.2924 | 0.3178 | 0.4078 | 0.5495 | 0.2397 | 0.8394 |
| 0.8032 | 7.5002 | 11505 | 0.1700 | 0.3056 | 0.3298 | 0.4215 | 0.5176 | 0.2636 | 0.8366 |
| 0.8074 | 8.0 | 12272 | 0.1688 | 0.2973 | 0.3267 | 0.4141 | 0.5121 | 0.2569 | 0.8347 |
| 0.6872 | 8.5002 | 13039 | 0.1763 | 0.3083 | 0.3363 | 0.4268 | 0.4972 | 0.2725 | 0.8331 |
| 0.6872 | 9.0 | 13806 | 0.1761 | 0.3008 | 0.3329 | 0.4197 | 0.4891 | 0.2651 | 0.8327 |
| 0.5634 | 9.5002 | 14573 | 0.1835 | 0.3032 | 0.3352 | 0.4194 | 0.4748 | 0.2755 | 0.8264 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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