Update README.md

README.md

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 license: cc-by-nc-4.0
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+
+## 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 `TweebankNLP` project, please refer to this [our paper](https://arxiv.org/pdf/2201.07281.pdf) and [github](https://github.com/social-machines/TweebankNLP) page.
+
+## How to use the model
+
+```python
+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](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}
+}
+```