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Congen WangchanBERT Small

Developed by kornwtp
This is a sentence embedding model based on the ConGen framework, capable of mapping sentences to a 128-dimensional dense vector space, suitable for tasks such as semantic search.
Downloads 812
Release Time : 5/11/2023

Model Overview

This model is a sentence transformer specifically designed for feature extraction and sentence similarity calculation, based on the WangchanBERT-Small architecture.

Model Features

Dense vector representation
Maps sentences to a 128-dimensional dense vector space, facilitating sentence similarity calculation.
Efficient feature extraction
Capable of quickly extracting sentence features, suitable for large-scale semantic search tasks.
Thai language optimization
Specifically optimized for Thai sentences, suitable for Thai semantic processing tasks.

Model Capabilities

Sentence embedding
Feature extraction
Sentence similarity calculation
Semantic search

Use Cases

Information retrieval
Semantic search
Use sentence embeddings for semantic search to improve the relevance of search results.
Natural language processing
Sentence similarity calculation
Calculate the semantic similarity between two sentences for text matching tasks.
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