annotations_creators:
- machine-generated
language_creators:
- machine-generated
widget:
- text: George Washington went to Washington.
- text: What is the seventh tallest mountain in North America?
tags:
- named-entity-recognition
- sequence-tagger-model
datasets:
- Babelscape/cner
language:
- en
pretty_name: cner-model
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
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 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 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.