---
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