Token Classification
Transformers
Safetensors
English
modernbert
semantic-highlighting
extractive-qa
evidence-selection
acl-anthology
custom_code
Instructions to use KRLabsOrg/acl-verbatim-modernbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRLabsOrg/acl-verbatim-modernbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("KRLabsOrg/acl-verbatim-modernbert", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Citation
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@misc{Recski:2026,
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title={ACL-Verbatim: hallucination-free question answering for research},
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author={Gábor Recski and Szilveszter Tóth and Nadia Verdha and István Boros and Ádám Kovács},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2605.21102},
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}
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## Citation
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```bibtex
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@misc{Recski:2026,
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title={ACL-Verbatim: hallucination-free question answering for research},
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author={Gábor Recski and Szilveszter Tóth and Nadia Verdha and István Boros and Ádám Kovács},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2605.21102},
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}
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```
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