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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_plus
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_plus
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: 0.4721
- Subset Accuracy: 0.2877
- F1 Macro: 0.3277
- F1 Micro: 0.3949
- Precision Macro: 0.4016
- Recall Macro: 0.2853
- Roc Auc: 0.8036
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 2025
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 25
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------:|
| 0.4892 | 1.0 | 767 | 0.1669 | 0.1642 | 0.1686 | 0.2704 | 0.4781 | 0.1215 | 0.8258 |
| 0.3063 | 2.0 | 1534 | 0.1582 | 0.2215 | 0.2160 | 0.3381 | 0.6175 | 0.1601 | 0.8524 |
| 0.2401 | 3.0 | 2301 | 0.1640 | 0.2410 | 0.2764 | 0.3530 | 0.4702 | 0.2059 | 0.8437 |
| 0.1323 | 4.0 | 3068 | 0.2049 | 0.2792 | 0.3119 | 0.3880 | 0.4098 | 0.2720 | 0.8240 |
| 0.0739 | 5.0 | 3835 | 0.2595 | 0.2836 | 0.3159 | 0.3939 | 0.4256 | 0.2737 | 0.8177 |
| 0.039 | 6.0 | 4602 | 0.2987 | 0.2799 | 0.3054 | 0.3942 | 0.4040 | 0.2615 | 0.8118 |
| 0.0249 | 7.0 | 5369 | 0.3283 | 0.2688 | 0.3133 | 0.3878 | 0.4067 | 0.2674 | 0.8116 |
| 0.0145 | 8.0 | 6136 | 0.3720 | 0.2694 | 0.3194 | 0.3941 | 0.3892 | 0.2965 | 0.8063 |
| 0.0109 | 9.0 | 6903 | 0.3840 | 0.2803 | 0.3197 | 0.4014 | 0.4034 | 0.2790 | 0.8075 |
| 0.0073 | 10.0 | 7670 | 0.4166 | 0.2746 | 0.3278 | 0.3892 | 0.3914 | 0.2942 | 0.8083 |
| 0.007 | 11.0 | 8437 | 0.4251 | 0.2773 | 0.3231 | 0.3861 | 0.4175 | 0.2749 | 0.8053 |
| 0.0046 | 12.0 | 9204 | 0.4406 | 0.2848 | 0.3220 | 0.3975 | 0.4023 | 0.2820 | 0.8065 |
| 0.0046 | 13.0 | 9971 | 0.4518 | 0.2794 | 0.3154 | 0.3872 | 0.4223 | 0.2615 | 0.8019 |
| 0.0044 | 14.0 | 10738 | 0.4822 | 0.2784 | 0.3119 | 0.3859 | 0.4071 | 0.2632 | 0.7930 |
| 0.0042 | 15.0 | 11505 | 0.4721 | 0.2877 | 0.3277 | 0.3949 | 0.4016 | 0.2853 | 0.8036 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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