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modernbert-pii-mapped-v2
jefftherover/modernbert-pii-mapped-v2
modernbert-pii-mapped-v2 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
- 5bf94c8d
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Base models
Model card
From the Hugging Face model README
<a href="https://huggingface.co/spaces/jefftherover/huggingface-static-c5ac60" 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-v2
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: 0.0073
- Precision: 0.9800
- Recall: 0.9887
- 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: 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: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0521 | 0.4087 | 500 | 0.0201 | 0.9174 | 0.9504 | 0.9336 | 0.9941 |
| 0.0206 | 0.8173 | 1000 | 0.0109 | 0.9437 | 0.9740 | 0.9586 | 0.9965 |
| 0.0175 | 1.2256 | 1500 | 0.0097 | 0.9625 | 0.9739 | 0.9682 | 0.9972 |
| 0.0117 | 1.6342 | 2000 | 0.0082 | 0.9650 | 0.9848 | 0.9748 | 0.9975 |
| 0.0083 | 2.0425 | 2500 | 0.0067 | 0.9690 | 0.9903 | 0.9795 | 0.9977 |
| 0.0065 | 2.4512 | 3000 | 0.0071 | 0.9697 | 0.9910 | 0.9802 | 0.9977 |
| 0.0062 | 2.8598 | 3500 | 0.0060 | 0.9806 | 0.9888 | 0.9847 | 0.9983 |
| 0.0030 | 3.2681 | 4000 | 0.0062 | 0.9807 | 0.9898 | 0.9853 | 0.9984 |
| 0.0024 | 3.6767 | 4500 | 0.0068 | 0.9794 | 0.9890 | 0.9842 | 0.9983 |
| 0.0008 | 4.0850 | 5000 | 0.0070 | 0.9801 | 0.9895 | 0.9848 | 0.9983 |
| 0.0006 | 4.4937 | 5500 | 0.0073 | 0.9800 | 0.9887 | 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