Instructions to use tceron/high-risk-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tceron/high-risk-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tceron/high-risk-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tceron/high-risk-classifier") model = AutoModelForSequenceClassification.from_pretrained("tceron/high-risk-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,175 Bytes
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library_name: transformers
license: odc-by
datasets:
- tceron/wildseek
language:
- en
metrics:
- f1
base_model:
- answerdotai/ModernBERT-large
pipeline_tag: text-classification
---
# Model Card for Model ID
This classifier classifier information seeking queries into 7 categories for high-risk sensitive queries.
Labels:
0: "Other",
1: "Economic and Financial",
2: "Health",
3: "Politics",
4: "Judicial and Legal",
5: "Moral Values and Religion",
6: "Security",
## Model Details
All details in [paper](https://arxiv.org/abs/2608.30683):
```bibtex
@inproceedings{ceron2026wildseek,
title = {WildSEEK: Evaluating Language Models for Information-Seeking},
author = {Ceron, Tanise and Baumann, Joachim and Bassignana, Elisa and Cabuk, Berat and Hovy, Dirk and Nozza, Debora},
booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
year = {2026}
}
```
## Other classifiers from this study are:
- **`tceron/info-seek-classifier`** — information seeking, content creation, coding, not english, no request
- **`tceron/open-endedness-classifier`** — Analytical, Factoid |