Cner Base

🚀 CNER: Concept and Named Entity Recognition

This model can jointly identify and classify concepts and named entities with fine - grained tags, offering a solution for named - entity recognition tasks.

🚀 Quick Start

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. So, the results of this model may differ from the ones presented in the paper.

The resulting CNER model is capable of 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.

✨ Features

• Named - Entity Recognition: The model is designed for named - entity recognition tasks, specifically for jointly identifying and classifying concepts and named entities.
• Fine - Grained Tagging: It can classify entities with fine - grained tags.

📦 Installation

The installation mainly involves setting up the necessary Python libraries. You can use pip to install the transformers library which is required to use this model:

pip install transformers

💻 Usage Examples

Basic Usage

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)

📚 Documentation

Classes

📄 License

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)](https://creativecommons.org/licenses/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.