Summary of Jina-embeddings-v3: Multilingual Embeddings with Task Lora, by Saba Sturua et al.
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
by Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Günther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang, Han Xiao
First submitted to arxiv on: 16 Sep 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 The novel text embedding model jina-embeddings-v3 achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes task-specific LoRA adapters for query-document retrieval, clustering, classification, and text matching. It outperforms OpenAI’s proprietary embeddings and Cohere’s models on English tasks, while achieving superior performance compared to multilingual-e5-large-instruct across all multilingual tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new text embedding model that does very well at understanding language. This helps computers do tasks like searching for information in long texts or grouping similar texts together. The model has special parts that help it get better at specific jobs, and it can even shrink its output size without losing performance. |
Keywords
» Artificial intelligence » Classification » Clustering » Embedding » Lora
