Babelscape/cner-base

CNER: Concept and Named Entity Recognition

This is the model card for the NAACL 2024 paper CNER: Concept and Named Entity Recognition. We fine-tuned a language model (DeBERTa-v3-base) for 1 epoch on our CNER dataset using the default hyperparameters, optimizer and architecture of Hugging Face, therefore the results of this model may differ from the ones presented in the paper. The resulting CNER model is able to jointly identifying and classifying concepts and named entities with fine-grained tags.

If you use the model, please reference this work in your paper:

@inproceedings{martinelli-etal-2024-cner,
    title = "{CNER}: Concept and Named Entity Recognition",
    author = "Martinelli, Giuliano  and
      Molfese, Francesco  and
      Tedeschi, Simone  and
      Fern{\'a}ndez-Castro, Alberte  and
      Navigli, Roberto",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.461",
    pages = "8329--8344",
}

The original repository for the paper can be found at https://github.com/Babelscape/cner.

How to use

You can use this model with Transformers NER pipeline.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Babelscape/cner-model")
model = AutoModelForTokenClassification.from_pretrained("Babelscape/cner-model")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "What is the seventh tallest mountain in North America?"

ner_results = nlp(example)
print(ner_results)

Classes

Licensing Information

Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents and models belongs to the original copyright holders.

microsoft/deberta-v3-base is released under the MIT license.