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modernbert-pii-mapped-v13
jefftherover/modernbert-pii-mapped-v13
modernbert-pii-mapped-v13 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
- Jun 3, 2026
- Updated
- Jun 3, 2026
- SHA
- c30de271
Try a prompt
Base models
Model card
From the Hugging Face model README
<a href="https://huggingface.co/spaces/jefftherover/huggingface-static-dbebfd" 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-v13
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: 0.0151
- Precision: 0.9599
- Recall: 0.9700
- F1: 0.9650
- Accuracy: 0.9966
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 |
|---|---|---|---|---|---|---|---|
| 0.1180 | 0.3322 | 500 | 0.0509 | 0.7814 | 0.8566 | 0.8173 | 0.9844 |
| 0.0591 | 0.6645 | 1000 | 0.0326 | 0.8853 | 0.9299 | 0.9070 | 0.9910 |
| 0.0383 | 0.9967 | 1500 | 0.0202 | 0.9219 | 0.9577 | 0.9395 | 0.9936 |
| 0.0359 | 1.3289 | 2000 | 0.0172 | 0.9408 | 0.9617 | 0.9511 | 0.9953 |
| 0.0233 | 1.6611 | 2500 | 0.0149 | 0.9469 | 0.9655 | 0.9561 | 0.9961 |
| 0.0226 | 1.9934 | 3000 | 0.0154 | 0.9458 | 0.9647 | 0.9551 | 0.9957 |
| 0.0137 | 2.3256 | 3500 | 0.0142 | 0.9583 | 0.9732 | 0.9657 | 0.9964 |
| 0.0112 | 2.6578 | 4000 | 0.0141 | 0.9542 | 0.9700 | 0.9620 | 0.9964 |
| 0.0105 | 2.9900 | 4500 | 0.0142 | 0.9523 | 0.9657 | 0.9589 | 0.9962 |
| 0.0049 | 3.3223 | 5000 | 0.0151 | 0.9599 | 0.9700 | 0.9650 | 0.9966 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Token Classification
- Library
- transformers
- Type
- modernbert
- License
- apache-2.0
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026