I

Intention

Developed by leeloolee
The GTE multilingual base model is a dense sentence transformer that supports sentence similarity calculation and text embedding tasks in multiple languages.
Downloads 32
Release Time : 9/7/2024

Model Overview

This model is a multilingual sentence transformer specifically designed for sentence similarity calculation and text embedding tasks across multiple languages. It supports over 50 languages and is suitable for applications such as cross-language information retrieval, text clustering, and classification.

Model Features

Multilingual support
Supports over 50 languages, suitable for cross-language text processing tasks.
Dense representation
Uses a dense transformer architecture to generate high-quality sentence embeddings.
Versatility
Suitable for various natural language processing tasks, including similarity calculation, clustering, classification, and information retrieval.

Model Capabilities

Sentence similarity calculation
Text embedding generation
Cross-language information retrieval
Text clustering
Text classification
Bilingual text mining

Use Cases

Information retrieval
Cross-language document retrieval
This model can be used to retrieve relevant documents in different languages.
Achieved an NDCG@10 score of 53.638 in the MTEB AlloprofRetrieval task
Text classification
Sentiment analysis
Can be used for multilingual sentiment classification tasks.
Achieved an accuracy of 80.72% in the MTEB AmazonPolarityClassification task
Text similarity
Sentence similarity calculation
Calculates semantic similarity between sentences in different languages.
Achieved a Spearman correlation coefficient of 81.21 for cosine similarity in the MTEB BIOSSES task
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