metadata
license: cc-by-nc-4.0
Model Specification
- This is the state-of-the-art Twitter NER model (with 74.35% Entity-Level F1) on Tweebank V2's NER benchmark (also called
Tweebank-NER), trained on the corpus combining both Tweebank-NER and WNUT 17 training data. - For more details about the
TweebankNLPproject, please refer to this our paper and github page. - In the paper, it is referred as
HuggingFace-BERTweet (TB2+W17).
How to use the model
- PRE-PROCESSING: when you apply the model on tweets, please make sure that tweets are preprocessed by the TweetTokenizer to get the best performance.
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner")
model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner")
References
If you use this repository in your research, please kindly cite our paper:
@article{jiang2022tweetnlp,
title={Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis},
author={Jiang, Hang and Hua, Yining and Beeferman, Doug and Roy, Deb},
journal={In Proceedings of the 13th Language Resources and Evaluation Conference (LREC)},
year={2022}
}