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modernbert-pii-mapped
jefftherover/modernbert-pii-mapped
modernbert-pii-mapped 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 5, 2026
- Updated
- May 5, 2026
- SHA
- 591776c8
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Base models
Model card
From the Hugging Face model README
<a href="https://huggingface.co/spaces/jefftherover/huggingface-static-a94390" 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-mapped
This model is a fine-tuned version of jefftherover/modernbert-pii-ner on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0104
- Precision: 0.9793
- Recall: 0.9893
- F1: 0.9843
- Accuracy: 0.9983
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: 3e-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: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0085 | 0.4087 | 500 | 0.0079 | 0.9700 | 0.9833 | 0.9766 | 0.9977 |
| 0.0055 | 0.8173 | 1000 | 0.0070 | 0.9770 | 0.9879 | 0.9824 | 0.9982 |
| 0.0045 | 1.2256 | 1500 | 0.0085 | 0.9722 | 0.9819 | 0.9770 | 0.9979 |
| 0.0037 | 1.6342 | 2000 | 0.0079 | 0.9749 | 0.9884 | 0.9816 | 0.9980 |
| 0.0022 | 2.0425 | 2500 | 0.0079 | 0.9775 | 0.9891 | 0.9833 | 0.9981 |
| 0.0012 | 2.4512 | 3000 | 0.0093 | 0.9745 | 0.9879 | 0.9812 | 0.9980 |
| 0.0011 | 2.8598 | 3500 | 0.0086 | 0.9778 | 0.9914 | 0.9846 | 0.9982 |
| 0.0002 | 3.2681 | 4000 | 0.0094 | 0.9814 | 0.9892 | 0.9853 | 0.9984 |
| 0.0001 | 3.6767 | 4500 | 0.0104 | 0.9796 | 0.9897 | 0.9846 | 0.9983 |
| 0.0000 | 4.0850 | 5000 | 0.0103 | 0.9793 | 0.9898 | 0.9845 | 0.9983 |
| 0.0000 | 4.4937 | 5500 | 0.0104 | 0.9793 | 0.9893 | 0.9843 | 0.9983 |
Framework versions
- Transformers 5.8.0
- 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 5, 2026
- Updated
- May 5, 2026