NV-Embed-v2
Maintainer: nvidia
115
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| 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 |
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Model overview
The NV-Embed-v2 model is a generalist embedding model developed by NVIDIA. It ranks first on the Massive Text Embedding Benchmark (MTEB benchmark) with a score of 72.31 across 56 text embedding tasks. The model also holds the top spot in the retrieval sub-category with a score of 62.65 across 15 tasks, which is essential for the development of Retrieval Augmented Generation (RAG) technology.
The NV-Embed-v2 model introduces several new architectural designs and training techniques, including having the Large Language Model (LLM) attend to latent vectors for better pooled embedding output, and a two-staged instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks. Additionally, the model incorporates a novel hard-negative mining method that takes into account the positive relevance score for better false negatives removal.
The NV-Embed-v2 model can be compared to similar models like NV-Embed-v1, all-mpnet-base-v2, paraphrase-multilingual-mpnet-base-v2, and e5-mistral-7b-instruct, all of which are focused on improving text embeddings using large language models.
Model inputs and outputs
Inputs
- Queries: Text queries that need to be accompanied by a corresponding instruction describing the task.
- Passages: Text passages that do not require any additional instruction.
Outputs
- Embeddings: The model generates dense vector embeddings for the input queries and passages, which can be used for tasks like information retrieval, clustering, or semantic search.
Capabilities
The NV-Embed-v2 model excels at a wide range of text embedding tasks, ranking first on the Massive Text Embedding Benchmark. It demonstrates strong performance in both retrieval and non-retrieval tasks, making it a versatile tool for various natural language processing applications.
What can I use it for?
The NV-Embed-v2 model can be used for a variety of tasks that require robust text embeddings, such as:
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Information Retrieval: The model's strong performance in the retrieval sub-category of the MTEB benchmark suggests it can be effectively used for tasks like passage retrieval, question answering, and document search.
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Semantic Similarity: The model's ability to generate high-quality sentence and paragraph embeddings can be leveraged for tasks like paraphrase detection, text clustering, and recommender systems.
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Downstream NLP Tasks: The embeddings generated by
NV-Embed-v2can be used as features for various downstream natural language processing tasks, such as classification, sentiment analysis, and named entity recognition.
Things to try
One interesting aspect of the NV-Embed-v2 model is its use of a two-staged instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks. This suggests that the model may be particularly well-suited for applications that require both precise information retrieval and robust semantic understanding, such as conversational AI systems or intelligent search engines.
Researchers and practitioners may want to explore how the model's instruction-based tuning approach can be leveraged to customize the embeddings for specific domains or use cases, potentially leading to further performance improvements on targeted tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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