Bertweet Tb2 Pos Tagging

🚀 Twitter POS Tagging Model

This is a baseline Twitter POS tagging model with 95.21% accuracy on Tweebank V2's NER benchmark. It provides a reliable foundation for Twitter POS tagging tasks.

🚀 Quick Start

Model Specification

• This is a baseline Twitter POS tagging model (with 95.21% Accuracy) on Tweebank V2's NER benchmark (also called Tweebank-NER), trained on the Tweebank-NER training data.
• If you are looking for the SOTA Twitter POS tagger, please go to this HuggingFace hub link.
• For more details about the TweebankNLP project, please refer to this our paper and github page.
• In the paper, it is referred as HuggingFace-BERTweet (TB2) in the POS table.

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.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2-pos-tagging")

model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2-pos-tagging")

📄 License

This project is licensed under the CC BY-NC 4.0 license.

📚 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}
}