Token Classification
Transformers
Safetensors
English
Chinese
xlm-roberta
pii
pii-detection
de-identification
anonymization
redaction
legal
ner
Instructions to use libraos/veil-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use libraos/veil-detect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="libraos/veil-detect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("libraos/veil-detect") model = AutoModelForTokenClassification.from_pretrained("libraos/veil-detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 584 Bytes
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Copyright 2026 Libra OS
This model was fine-tuned from FacebookAI/xlm-roberta-base (MIT License).
Training data includes:
- Nemotron-PII by NVIDIA Corporation, licensed under CC BY 4.0
(https://huggingface.co/datasets/nvidia/Nemotron-PII,
https://creativecommons.org/licenses/by/4.0/). Used to train this model; no records are redistributed.
- gravitee-io/pii-detection-dataset (Apache-2.0) and its upstream sources.
- gretelai/synthetic_pii_finance_multilingual (Apache-2.0).
- CLUENER2020 (Apache-2.0).
- Text Anonymization Benchmark, TAB (MIT), train split only.
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