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Sanstib

Developed by buddhist-nlp
This model is used to generate sentence embeddings for Sanskrit and Tibetan, suitable for semantic similarity tasks.
Downloads 14
Release Time : 4/22/2022

Model Overview

This model can convert Sanskrit and Tibetan sentences into vector representations, primarily used for computing semantic similarity between sentences. Sanskrit requires tokenization and conversion to an internal transliteration format, while Tibetan needs to be converted to Wylie transliteration format.

Model Features

Multilingual support
Supports sentence embedding generation for both Sanskrit and Tibetan languages.
Semantic similarity computation
Capable of computing semantic similarity between sentences, suitable for various natural language processing tasks.
Preprocessing requirements
Sanskrit requires tokenization and conversion to an internal transliteration format, while Tibetan needs to be converted to Wylie transliteration format.

Model Capabilities

Generate sentence embeddings
Compute semantic similarity

Use Cases

Natural Language Processing
Semantic search
Used for semantic search in Sanskrit and Tibetan texts to improve the relevance of search results.
Text clustering
Perform clustering analysis on Sanskrit and Tibetan texts to discover themes or patterns within the texts.
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