readme: ---
  license: cc-by-nc-4.0
  ---
  
  ## Model Specification
  - This is one **baseline Twitter NER model (with 73.71\% Entity-Level F1)** 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 NER model**, please go to this [HuggingFace hub link](https://huggingface.co/TweebankNLP/bertweet-tb2_wnut17-ner).
  - 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. 
  - In the paper, it is referred as `HuggingFace-BERTweet (TB2)` in the NER 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](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) to get the best performance.
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2-ner")
  
  model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2-ner")
  ```
  
  ## References
  
  If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf): 
  
  ```bibtex
  @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}
  }
  ```