{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/irlab-iitbhu-at-wnut-2020-task-2","title":"IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT","arxiv_id":null,"date":"2020-11-01","proceeding":"EMNLP (WNUT) 2020 11","authors":["Supriya Chanda","Eshita Nandy","Sukomal Pal"],"abstract":"This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.","url_abs":"https://aclanthology.org/2020.wnut-1.56","url_pdf":"https://aclanthology.org/2020.wnut-1.56.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"irlab-iitbhu-at-wnut-2020-task-2","repo_url":"https://github.com/VinAIResearch/COVID19Tweet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}