| # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets | |
| BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the **COVID-19** pandemic. The general architecture and experimental results of BERTweet can be found in our [paper](https://aclanthology.org/2020.emnlp-demos.2/): | |
| @inproceedings{bertweet, | |
| title = {{BERTweet: A pre-trained language model for English Tweets}}, | |
| author = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen}, | |
| booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, | |
| pages = {9--14}, | |
| year = {2020} | |
| } | |
| **Please CITE** our paper when BERTweet is used to help produce published results or is incorporated into other software. | |
| For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)! | |
| ### Main results | |
| <p float="left"> | |
| <img width="275" alt="postagging" src="https://user-images.githubusercontent.com/2412555/135724590-01d8d435-262d-44fe-a383-cd39324fe190.png" /> | |
| <img width="275" alt="ner" src="https://user-images.githubusercontent.com/2412555/135724598-1e3605e7-d8ce-4c5e-be4a-62ae8501fae7.png" /> | |
| </p> | |
| <p float="left"> | |
| <img width="275" alt="sentiment" src="https://user-images.githubusercontent.com/2412555/135724597-f1981f1e-fe73-4c03-b1ff-0cae0cc5f948.png" /> | |
| <img width="275" alt="irony" src="https://user-images.githubusercontent.com/2412555/135724595-15f4f2c8-bbb6-4ee6-82a0-034769dec183.png" /> | |
| </p> | |