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modernbert-pii-ner
jefftherover/modernbert-pii-ner
modernbert-pii-ner is a token classification model from jefftherover. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- modernbert
- Access
- Public
- Created
- May 2, 2026
- Updated
- May 4, 2026
- SHA
- 6997de21
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Base models
Model card
From the Hugging Face model README
<a href="https://huggingface.co/spaces/jefftherover/huggingface-static-6bdbc7" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a>
<!-- 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-pii-ner
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1431
- Precision: 0.9096
- Recall: 0.9466
- F1: 0.9277
- Accuracy: 0.9683
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: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 0.2
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4505 | 0.4087 | 500 | 0.2343 | 0.6446 | 0.7708 | 0.7020 | 0.9337 |
| 0.2362 | 0.8173 | 1000 | 0.1324 | 0.7922 | 0.8659 | 0.8274 | 0.9299 |
| 0.1772 | 1.2256 | 1500 | 0.0934 | 0.8931 | 0.9199 | 0.9063 | 0.9640 |
| 0.1567 | 1.6342 | 2000 | 0.0807 | 0.8926 | 0.9343 | 0.9130 | 0.9653 |
| 0.1416 | 2.0425 | 2500 | 0.0772 | 0.8626 | 0.9288 | 0.8945 | 0.9570 |
| 0.1197 | 2.4512 | 3000 | 0.0763 | 0.8855 | 0.9370 | 0.9105 | 0.9643 |
| 0.1091 | 2.8598 | 3500 | 0.0731 | 0.9072 | 0.9473 | 0.9268 | 0.9688 |
| 0.0868 | 3.2681 | 4000 | 0.0774 | 0.9155 | 0.9486 | 0.9317 | 0.9688 |
| 0.0681 | 3.6767 | 4500 | 0.0880 | 0.9098 | 0.9483 | 0.9286 | 0.9684 |
| 0.0263 | 4.0850 | 5000 | 0.1211 | 0.9102 | 0.9460 | 0.9278 | 0.9674 |
| 0.0187 | 4.4937 | 5500 | 0.1431 | 0.9096 | 0.9466 | 0.9277 | 0.9683 |
Framework versions
- Transformers 5.7.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Token Classification
- Library
- transformers
- Type
- modernbert
- License
- apache-2.0
- Created
- May 2, 2026
- Updated
- May 4, 2026