gte-Qwen2-7B-instruct
Maintainer: Alibaba-NLP
103
🤯
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family developed by Alibaba-NLP. It ranks #1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark (MTEB) as of June 16, 2024. The model is based on the Qwen2-7B large language model, and builds upon the previous gte-Qwen1.5-7B-instruct model by incorporating several key advancements, including bidirectional attention mechanisms for enhanced contextual understanding, and instruction tuning applied solely on the query side for streamlined efficiency.
Model inputs and outputs
The gte-Qwen2-7B-instruct model takes text inputs and produces contextual embeddings. It can handle a wide range of text, from short queries to lengthy documents, with a maximum input length of 32,000 tokens.
Inputs
- Text data, such as sentences, paragraphs, or documents
Outputs
- Contextual embeddings, a high-dimensional vector representation of the input text
- The model outputs embeddings with a dimensionality of 3,584
Capabilities
The gte-Qwen2-7B-instruct model excels at a variety of text-related tasks, including semantic search, text classification, and data augmentation. Its comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios makes it highly applicable across numerous languages and a wide array of downstream tasks.
What can I use it for?
The gte-Qwen2-7B-instruct model can be leveraged for a wide range of applications, such as:
- Semantic search: Use the model's contextual embeddings to power semantic search engines, allowing users to find relevant information based on the meaning of their queries, not just keyword matching.
- Text classification: Fine-tune the model for specialized text classification tasks, such as sentiment analysis, topic classification, or intent detection.
- Data augmentation: Leverage the model's understanding of language to generate synthetic text data, which can be used to expand and diversify training datasets for machine learning models.
Things to try
One interesting aspect of the gte-Qwen2-7B-instruct model is its ability to handle long-form text inputs. Try using the model to generate embeddings for lengthy documents, such as research papers or technical manuals, and explore how the contextual understanding can be applied to tasks like document summarization or knowledge extraction.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
📈
gte-Qwen2-1.5B-instruct
72
gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. This model is built on the Qwen2-1.5B LLM model and uses the same training data and strategies as the gte-Qwen2-7B-instruct model. The model incorporates several key advancements, including the integration of bidirectional attention mechanisms, instruction tuning, and comprehensive training across a vast, multilingual text corpus. Model inputs and outputs Inputs Text inputs of up to 32,000 tokens Outputs Contextualized text embeddings with a dimension of 1,536 Capabilities The gte-Qwen2-1.5B-instruct model has been trained to excel at a wide range of natural language processing tasks, including text classification, clustering, retrieval, and similarity measurement. Its robust contextual understanding and multi-lingual capabilities make it a powerful tool for various applications. What can I use it for? The gte-Qwen2-1.5B-instruct model can be used for a variety of applications, such as semantic search, text classification, and text similarity. Its large model size and extensive training make it suitable for tasks that require robust language understanding and generalization, such as document retrieval, question answering, and content recommendation. Things to try One interesting aspect of the gte-Qwen2-1.5B-instruct model is its ability to handle long-form text inputs. By supporting a maximum input length of 32,000 tokens, the model can be used for tasks that require processing of lengthy documents or passages, such as summarization or knowledge extraction from research papers or legal contracts.
Updated Invalid Date
🏅
gte-Qwen1.5-7B-instruct
50
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.
Updated Invalid Date
🤯
gte-Qwen2-7B-instruct
103
gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family developed by Alibaba-NLP. It ranks #1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark (MTEB) as of June 16, 2024. The model is based on the Qwen2-7B large language model, and builds upon the previous gte-Qwen1.5-7B-instruct model by incorporating several key advancements, including bidirectional attention mechanisms for enhanced contextual understanding, and instruction tuning applied solely on the query side for streamlined efficiency. Model inputs and outputs The gte-Qwen2-7B-instruct model takes text inputs and produces contextual embeddings. It can handle a wide range of text, from short queries to lengthy documents, with a maximum input length of 32,000 tokens. Inputs Text data, such as sentences, paragraphs, or documents Outputs Contextual embeddings, a high-dimensional vector representation of the input text The model outputs embeddings with a dimensionality of 3,584 Capabilities The gte-Qwen2-7B-instruct model excels at a variety of text-related tasks, including semantic search, text classification, and data augmentation. Its comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios makes it highly applicable across numerous languages and a wide array of downstream tasks. What can I use it for? The gte-Qwen2-7B-instruct model can be leveraged for a wide range of applications, such as: Semantic search**: Use the model's contextual embeddings to power semantic search engines, allowing users to find relevant information based on the meaning of their queries, not just keyword matching. Text classification**: Fine-tune the model for specialized text classification tasks, such as sentiment analysis, topic classification, or intent detection. Data augmentation**: Leverage the model's understanding of language to generate synthetic text data, which can be used to expand and diversify training datasets for machine learning models. Things to try One interesting aspect of the gte-Qwen2-7B-instruct model is its ability to handle long-form text inputs. Try using the model to generate embeddings for lengthy documents, such as research papers or technical manuals, and explore how the contextual understanding can be applied to tasks like document summarization or knowledge extraction.
Updated Invalid Date
🏅
gte-Qwen1.5-7B-instruct
50
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.
Updated Invalid Date