| # shrugging-grace/tweetclassifier | |
| ## Model description | |
| This model classifies tweets as either relating to the Covid-19 pandemic or not. | |
| ## Intended uses & limitations | |
| It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Ministers' Questions. | |
| #### How to use | |
| ``LABEL_0`` means that the tweet relates to Covid-19 | |
| ``LABEL_1`` means that the tweet does not relate to Covid-19 | |
| ## Training data | |
| The model was trained on 1000 tweets (with the "#PMQs'), which were manually labeled by the author. The tweets were collected between May-July 2020. | |
| ### BibTeX entry and citation info | |
| This was based on a pretrained version of BERT. | |
| @article{devlin2018bert, | |
| title={Bert: Pre-training of deep bidirectional transformers for language understanding}, | |
| author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, | |
| journal={arXiv preprint arXiv:1810.04805}, | |
| year={2018} | |
| } | |