# Multilingual Text Embedding

This is the quantized ONNX version of Alibaba-NLP/gte-multilingual-reranker-base, utilizing INT8 quantization, optimized for GPU, and suitable for text classification tasks.

Multilingual E5 large instruction model supporting text embedding and classification tasks for over 100 languages

Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages

Multilingual E5 large instruction model, supporting multiple languages and tasks, including classification, retrieval, clustering, etc.

Multilingual E5 large instruction model, supporting multiple languages and tasks including classification, retrieval, clustering, etc.

Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages

Multilingual E5 Large is a multilingual sentence transformer model focused on sentence similarity and feature extraction tasks, supporting multiple languages.

Multilingual sentence embedding model supporting text similarity computation and semantic search for 50+ languages

A multilingual text vectorization model focused on sentence similarity calculation and feature extraction tasks

A locally sparse global version based on intfloat/multilingual-e5-small, supporting multilingual text embedding models with approximately 4k tokens

A multilingual text embedding model fine-tuned on XNLI and ANLI datasets based on intfloat/multilingual-e5-base, suitable for zero-shot classification tasks

A multilingual text classification model fine-tuned on the XNLI dataset based on multilingual-e5-large, supporting zero-shot classification in 15 languages

E5-base-multilingual-4096 is a locally sparse global version based on intfloat/multilingual-e5-base, supporting multilingual text embedding models that can process up to 4096 tokens.

A model based on distilbert-base-multilingual, fine-tuned to align the embedding space for 40 languages, matching the embedding space of CLIP text encoder.