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Sentence Embedding

The vector representation of sentence-level text is outputted to capture semantic similarity between texts. Suitable for clustering, regression, anomaly detection, visualization and other tasks.

Product Overview

Sentence Embedding

We can output vectors of sentence-level text, which can capture semantic similarity between texts. Suitable for clustering, regression, anomaly detection, visualization and other tasks.

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Technical Advantages

Leading Lightweight Technology

Achieving specific model effect and greatly reducing the use cost of pre-trained model service with a smaller model.

Optimization for Chinese

Based on the self-built Chinese multi-domain dataset, the model effect is more in line with the Chinese language characteristics.

Small/Zero Sample Capability

Prompt technology can flexibly support various Chinese generative tasks, and achieve SOTA performance in many zero-sample tasks.

Application Scenarios

Analysis of User Evaluation

Multi-dimensional analysis of user evaluation in different industries, including user reputation judgment, user opinion extraction and classification, emotional tendency judgment, etc.

Search and Recommend

Establishing user interest representation, calculating user preference and content matching degree, realizing user-oriented search, displaying content personalization.

Text Classification

Classifying the text in a variety of scenarios to improve the text processing effect.

Get Started with the Sentence Embedding Model

Products

Business Cooperation Email

bd@langboat.com

ewm

Address

Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.


© 2023, Langboat Co., Limited. All rights reserved.

Business Cooperation:

bd@langboat.com

Address:

Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.

Official Accounts:

ewm

© 2023, Langboat Co., Limited. All rights reserved.
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