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Fact Or Opinion Xlmr El

Developed by lighteternal
This is a binary classification model based on the XLM-Roberta-base architecture, capable of classifying sentences as facts or opinions. It supports English and Greek, and features zero-shot learning capability.
Downloads 1,051
Release Time : 3/2/2022

Model Overview

This model is designed for text classification tasks, specifically to distinguish factual statements from subjective opinions. Jointly developed by the Hellenic Army Academy and the Technical University of Crete, it is trained on a mixed English-Greek annotated corpus.

Model Features

Bilingual training
The model is trained on a mixed English and Greek dataset, enabling bilingual processing capabilities
Zero-shot learning
Supports classification tasks in any language supported by XLM-R without language-specific training
High accuracy
Achieves an F1 score of 0.952 on test sets, demonstrating excellent performance

Model Capabilities

Text classification
Fact-checking
Opinion recognition
Multilingual processing

Use Cases

Content moderation
News fact-checking
Automatically identifies factual statements and opinion expressions in news content
Improves efficiency in verifying news authenticity
Academic research
Literature analysis
Distinguishes objective facts from subjective opinions in academic literature
Assists researchers in quickly locating key information
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