This SciBert-based multi-label classifier, trained as part of the work "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse", distinguishes three different forms of science-relatedness for Tweets. See details at https://github.com/AI-4-Sci/SciTweets .
{"license": "cc-by-4.0", "widget": [{"text": "Study: Shifts in electricity generation spur net job growth, but coal jobs decline - via @DukeU https://www.eurekalert.org/news-releases/637217", "example_title": "All categories"}, {"text": "Shifts in electricity generation spur net job growth, but coal jobs decline", "example_title": "Only Cat 1.1"}, {"text": "Study on impacts of electricity generation shift via @DukeU https://www.eurekalert.org/news-releases/637217", "example_title": "Only Cat 1.2 and 1.3"}, {"text": "@DukeU received grant for research on electricity generation shift", "example_title": "Only Cat 1.3"}]}
transformers pytorch bert text-classification license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

This SciBert-based multi-label classifier, trained as part of the work "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse", distinguishes three different forms of science-relatedness for Tweets. See details at URL .
TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
