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Abstract Sim Sentence

Developed by biu-nlp
A model that maps abstract sentence descriptions to matching sentences, trained on Wikipedia using a dual encoder architecture.
Downloads 51
Release Time : 5/13/2023

Model Overview

This model is used to map abstract sentence descriptions to matching sentences, primarily for sentence similarity computation and feature extraction tasks.

Model Features

Dual encoder architecture
Employs independent query encoder and sentence encoder to process queries and sentences separately, improving matching accuracy.
Wikipedia-based training
Trained on Wikipedia data, capable of handling broad semantic information.
Efficient feature extraction
Capable of efficiently extracting sentence features for similarity computation or other downstream tasks.

Model Capabilities

Sentence feature extraction
Sentence similarity computation
Abstract sentence matching

Use Cases

Information retrieval
Company relationship query
Matches relevant sentences based on abstract queries (e.g., 'a company is part of a larger company').
Can accurately match sentences describing company relationships, such as subsidiaries, parent companies, etc.
Semantic search
Abstract query matching
Maps abstract queries to specific relevant sentences.
Can effectively distinguish between relevant and irrelevant sentences, with ranking results meeting expectations.
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