Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen
We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at https://github.com/VinAIResearch/BERTweet
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Part-Of-Speech Tagging | Ritter | Acc | 90.1 | BERTweet |
| Part-Of-Speech Tagging | Tweebank | Acc | 95.2 | BERTweet |
| Sentiment Analysis | TweetEval | ALL | 67.9 | BERTweet |
| Sentiment Analysis | TweetEval | Emoji | 33.4 | BERTweet |
| Sentiment Analysis | TweetEval | Emotion | 79.3 | BERTweet |
| Sentiment Analysis | TweetEval | Irony | 82.1 | BERTweet |
| Sentiment Analysis | TweetEval | Offensive | 79.5 | BERTweet |
| Sentiment Analysis | TweetEval | Sentiment | 73.4 | BERTweet |
| Sentiment Analysis | TweetEval | Stance | 71.2 | BERTweet |
| Named Entity Recognition (NER) | WNUT 2017 | F1 | 56.5 | BERTweet |
| Named Entity Recognition (NER) | WNUT 2016 | F1 | 52.1 | BERTweet |