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Negmpnet

Developed by tum-nlp
NegMPNet is a negation-aware version based on all-mpnet-base-v2, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, particularly adept at handling negation semantics.
Downloads 31
Release Time : 7/14/2023

Model Overview

This is a sentence-transformers model, specifically optimized for negation semantics, suitable for tasks such as sentence similarity calculation and semantic search.

Model Features

Negation Awareness
Compared to the base model, it is more sensitive to negation semantics, better distinguishing between affirmative and negative sentences.
High-Dimensional Vector Representation
Can map sentences and paragraphs into a 768-dimensional dense vector space.
Sentence Similarity Calculation
Specially optimized for sentence similarity calculation tasks, especially for sentences containing negation semantics.

Model Capabilities

Sentence Embedding
Semantic Similarity Calculation
Feature Extraction
Negation Semantics Recognition

Use Cases

Text Processing
Semantic Search
Can be used to build semantic search engines, especially in scenarios requiring differentiation between affirmative and negation semantics.
Can more accurately distinguish between queries and documents containing negation words.
Text Clustering
Perform clustering analysis on texts, especially those requiring differentiation between affirmative and negative expressions.
Can better distinguish between texts expressing opposite meanings.
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