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license: cc-by-nc-4.0 |
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--- |
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## Model Specification |
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- 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. |
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- For more details about the `TweebankNLP` project, please refer to this [our paper](https://arxiv.org/pdf/2201.07281.pdf) and [github](https://github.com/social-machines/TweebankNLP) page. |
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- In the paper, it is referred as `HuggingFace-BERTweet (TB2)` in the POS table. |
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## How to use the model |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner") |
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model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner") |
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``` |
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## References |
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If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf): |
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```bibtex |
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@article{jiang2022tweetnlp, |
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title={Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis}, |
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author={Jiang, Hang and Hua, Yining and Beeferman, Doug and Roy, Deb}, |
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journal={In Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, |
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year={2022} |
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} |
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``` |
