
## 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}
}
```
{"license": "cc-by-nc-4.0"}
transformers pytorch roberta token-classification arxiv:2201.07281 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 


## 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.
- 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 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 to get the best performance.



## References

If you use this repository in your research, please kindly cite our paper: 


## 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.
- 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 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 to get the best performance. ## References

If you use this repository in your research, please kindly cite our paper:
TAGS
#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## 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.
- 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 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 to get the best performance. ## References

If you use this repository in your research, please kindly cite our paper:
passage: TAGS
#transformers #pytorch #roberta #token-classification #arxiv-2201.07281 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
## 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.
- 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 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 to get the best performance.## References

If you use this repository in your research, please kindly cite our paper: