Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Paper • 2104.09947 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/mbert-corona-tweets-belgium-topics")
model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/mbert-corona-tweets-belgium-topics")Quick Links
Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
Models used in this paper are on HuggingFace:
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DTAI-KULeuven/mbert-corona-tweets-belgium-topics")