gte-Qwen1.5-7B-instruct

Maintainer: Alibaba-NLP

Total Score

50

Last updated 5/15/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

gte-Qwen1.5-7B-instruct is the latest addition to the gte embedding family from Alibaba-NLP. Built upon the robust natural language processing capabilities of the Qwen1.5-7B model, it incorporates several key advancements. These include the integration of bidirectional attention mechanisms to enrich its contextual understanding, as well as instruction tuning applied solely on the query side for streamlined efficiency. The model has also been comprehensively trained across a vast, multilingual text corpus spanning diverse domains and scenarios.

Model Inputs and Outputs

gte-Qwen1.5-7B-instruct is a powerful text embedding model that can handle a wide range of inputs, from short queries to longer text passages. The model supports a maximum input length of 32k tokens, making it suitable for a variety of natural language processing tasks.

Inputs

  • Text sequences of up to 32,000 tokens

Outputs

  • High-dimensional vector representations (embeddings) of the input text, with a dimension of 4096

Capabilities

The enhancements made to gte-Qwen1.5-7B-instruct allow it to excel at a variety of natural language processing tasks. Its robust contextual understanding and multilingual training make it a versatile tool for applications such as semantic search, text classification, and language generation.

What Can I Use It For?

gte-Qwen1.5-7B-instruct can be leveraged for a wide range of applications, from building personalized recommendations to powering multilingual chatbots. Its state-of-the-art performance on the MTEB benchmark, as demonstrated by the gte-base-en-v1.5 and gte-large-en-v1.5 models, makes it a compelling choice for embedding-based tasks.

Things to Try

Experiment with gte-Qwen1.5-7B-instruct to unlock its full potential. Utilize the model's robust contextual understanding and multilingual capabilities to tackle complex natural language processing challenges, such as cross-lingual information retrieval or multilingual sentiment analysis.



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gte-Qwen1.5-7B-instruct

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gte-Qwen1.5-7B-instruct is the latest addition to the gte embedding family from Alibaba-NLP. Built upon the robust natural language processing capabilities of the Qwen1.5-7B model, it incorporates several key advancements. These include the integration of bidirectional attention mechanisms to enrich its contextual understanding, as well as instruction tuning applied solely on the query side for streamlined efficiency. The model has also been comprehensively trained across a vast, multilingual text corpus spanning diverse domains and scenarios. Model Inputs and Outputs gte-Qwen1.5-7B-instruct is a powerful text embedding model that can handle a wide range of inputs, from short queries to longer text passages. The model supports a maximum input length of 32k tokens, making it suitable for a variety of natural language processing tasks. Inputs Text sequences of up to 32,000 tokens Outputs High-dimensional vector representations (embeddings) of the input text, with a dimension of 4096 Capabilities The enhancements made to gte-Qwen1.5-7B-instruct allow it to excel at a variety of natural language processing tasks. Its robust contextual understanding and multilingual training make it a versatile tool for applications such as semantic search, text classification, and language generation. What Can I Use It For? gte-Qwen1.5-7B-instruct can be leveraged for a wide range of applications, from building personalized recommendations to powering multilingual chatbots. Its state-of-the-art performance on the MTEB benchmark, as demonstrated by the gte-base-en-v1.5 and gte-large-en-v1.5 models, makes it a compelling choice for embedding-based tasks. Things to Try Experiment with gte-Qwen1.5-7B-instruct to unlock its full potential. Utilize the model's robust contextual understanding and multilingual capabilities to tackle complex natural language processing challenges, such as cross-lingual information retrieval or multilingual sentiment analysis.

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