{"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/infominer-at-wnut-2020-task-2-transformer","title":"InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction","arxiv_id":"2010.05327","date":"2020-10-11","proceeding":"EMNLP (WNUT) 2020 11","authors":["Hansi Hettiarachchi","Tharindu Ranasinghe"],"abstract":"Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set.","url_abs":"https://arxiv.org/abs/2010.05327v1","url_pdf":"https://arxiv.org/pdf/2010.05327v1.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":"infominer-at-wnut-2020-task-2-transformer","repo_url":"https://github.com/hhansi/informative-tweet-identification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.05327","atlas_url":"https://app.syntology.ai/?focus=2010.05327","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}