Summary of Arctic-embed: Scalable, Efficient, and Accurate Text Embedding Models, by Luke Merrick et al.
Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
by Luke Merrick, Danmei Xu, Gaurav Nuti, Daniel Campos
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the creation and recipe behind a family of text embedding models called arctic-embed, comprising five models with varying sizes ranging from 22 to 334 million parameters. The models’ weights are open-sourced under an Apache-2 license, allowing for their use in various applications. The report highlights that each model achieved state-of-the-art retrieval accuracy on the MTEB Retrieval leaderboard at the time of its release, outperforming closed-source embedding models such as Cohere’s embed-v3 and Open AI’s text-embed-3-large. Additionally, the paper provides informative ablation studies to shed light on the cause of the model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This report shares a secret recipe for creating powerful text embeddings called arctic-embed. These models are like super-smart librarians that help computers understand and organize lots of text data. The creators made five different versions, each with its own strengths and weaknesses. They even shared the codes so others can use them too! What’s special is that these models did better than some other secret ones when tested on a big challenge called MTEB Retrieval. |
Keywords
» Artificial intelligence » Embedding
