C

Covid Twitter Bert V2 Mnli

Developed by digitalepidemiologylab
A BERT-based zero-shot classifier optimized for analyzing COVID-19-related Twitter content
Downloads 142
Release Time : 3/2/2022

Model Overview

This model provides a zero-shot classifier suitable for scenarios where labeled data is insufficient to fine-tune CT-BERT for specific tasks. It can be directly used as a zero-shot classifier by reformulating classification tasks as questions.

Model Features

Zero-shot classification capability
Performs classification tasks without labeled data by directly reasoning through reformulated questions
COVID-19 domain optimization
Specifically optimized for COVID-19-related Twitter content
MNLI fine-tuning
Fine-tuned on 400,000 MNLI task entries, equipped with strong logical reasoning capabilities

Model Capabilities

Text classification
Zero-shot learning
Natural language inference

Use Cases

Public health
Vaccine-related tweet classification
Automatically identifies vaccine-related content on Twitter
Epidemic information monitoring
Analyzes trends in epidemic-related information on social media
Social media analysis
Topic classification
Automatically classifies COVID-19-related tweets
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