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library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: modern-bert-seq-class-values-no-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. -->
# modern-bert-seq-class-values-no-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: 0.4170
- Subset Accuracy: 0.2778
- F1 Macro: 0.3294
- F1 Micro: 0.3990
- Precision Macro: 0.4078
- Recall Macro: 0.2869
- Roc Auc: 0.8058
## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 2025
- gradient_accumulation_steps: 4
- 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: 0.1
- num_epochs: 20
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------:|
| 1.1783 | 0.5002 | 767 | 0.1854 | 0.0679 | 0.0755 | 0.1256 | 0.3030 | 0.0466 | 0.7610 |
| 0.6868 | 1.0 | 1534 | 0.1661 | 0.1378 | 0.1564 | 0.2358 | 0.5075 | 0.1038 | 0.8281 |
| 0.6248 | 1.5002 | 2301 | 0.1613 | 0.2025 | 0.2269 | 0.3192 | 0.5102 | 0.1713 | 0.8416 |
| 0.6101 | 2.0 | 3068 | 0.1561 | 0.2364 | 0.2349 | 0.3546 | 0.5593 | 0.1735 | 0.8521 |
| 0.4834 | 2.5002 | 3835 | 0.1669 | 0.2506 | 0.2784 | 0.3696 | 0.5196 | 0.2150 | 0.8427 |
| 0.4572 | 3.0 | 4602 | 0.1663 | 0.2645 | 0.2952 | 0.3769 | 0.5287 | 0.2266 | 0.8445 |
| 0.2812 | 3.5002 | 5369 | 0.2168 | 0.2803 | 0.3263 | 0.3868 | 0.3978 | 0.2925 | 0.8280 |
| 0.2386 | 4.0 | 6136 | 0.2064 | 0.2894 | 0.3236 | 0.4024 | 0.4280 | 0.2785 | 0.8263 |
| 0.1431 | 4.5002 | 6903 | 0.2515 | 0.2842 | 0.3292 | 0.3992 | 0.4010 | 0.2898 | 0.8179 |
| 0.123 | 5.0 | 7670 | 0.2675 | 0.2736 | 0.3156 | 0.3847 | 0.3955 | 0.2744 | 0.8145 |
| 0.0905 | 5.5002 | 8437 | 0.3188 | 0.2851 | 0.3272 | 0.3999 | 0.3789 | 0.2947 | 0.8143 |
| 0.0678 | 6.0 | 9204 | 0.3120 | 0.2762 | 0.3217 | 0.3860 | 0.4000 | 0.2736 | 0.8112 |
| 0.0544 | 6.5002 | 9971 | 0.3340 | 0.2847 | 0.3330 | 0.4064 | 0.4040 | 0.2937 | 0.8085 |
| 0.0395 | 7.0 | 10738 | 0.3480 | 0.2811 | 0.3341 | 0.3964 | 0.3944 | 0.2954 | 0.8096 |
| 0.0257 | 7.5002 | 11505 | 0.3684 | 0.2865 | 0.3275 | 0.4046 | 0.4225 | 0.2841 | 0.8056 |
| 0.0291 | 8.0 | 12272 | 0.3824 | 0.2821 | 0.3210 | 0.3994 | 0.4053 | 0.2835 | 0.8038 |
| 0.0173 | 8.5002 | 13039 | 0.3921 | 0.2821 | 0.3194 | 0.3945 | 0.4329 | 0.2679 | 0.8053 |
| 0.0157 | 9.0 | 13806 | 0.4061 | 0.2829 | 0.3307 | 0.4065 | 0.3905 | 0.2954 | 0.8035 |
| 0.0123 | 9.5002 | 14573 | 0.4170 | 0.2778 | 0.3294 | 0.3990 | 0.4078 | 0.2869 | 0.8058 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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