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Wrapresentations

Developed by TomatenMarc
WRAPresentations is an advanced sentence transformer model specifically designed for Twitter argument mining, capable of mapping tweets into four categories: 'Reason', 'Statement', 'Notification', and 'Nonsense'.
Downloads 268
Release Time : 8/2/2023

Model Overview

This model encodes tweets into four categories using a 768-dimensional dense vector space, derived from the BERTweet-base architecture and fine-tuned on the TACO dataset, effectively capturing reasoning and information in tweets.

Model Features

Twitter-Specific Argument Mining
Optimized specifically for Twitter content, effectively identifying and analyzing argument structures in tweets.
Four-Category Encoding
Accurately classifies tweets into four semantic types: 'Reason', 'Statement', 'Notification', and 'Nonsense'.
Contrastive Learning Optimization
Fine-tuned through contrastive learning to cluster similar tweets more closely in the embedding space.

Model Capabilities

Tweet Semantic Encoding
Argument Structure Analysis
Tweet Classification
Sentence Similarity Calculation

Use Cases

Social Media Analysis
Political Issue Argument Mining
Analyzing argument structures of different stances in political topics like Brexit
Effectively distinguishes factual statements from personal opinions
Hot Topic Monitoring
Tracking the evolution of arguments on hot topics in social media
Identifies core arguments and information sources in topic discussions
Content Moderation
Nonsense Content Filtering
Automatically identifying and filtering tweets with no substantive content
Improves content moderation efficiency
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