Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910439380615168 |
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| author | Merrick, Luke Xu, Danmei Nuti, Gaurav Campos, Daniel |
| author_facet | Merrick, Luke Xu, Danmei Nuti, Gaurav Campos, Daniel |
| contents | This report describes the training dataset creation and recipe behind the family of \texttt{arctic-embed} text embedding models (a set of five models ranging from 22 to 334 million parameters with weights open-sourced under an Apache-2 license). At the time of their release, each model achieved state-of-the-art retrieval accuracy for models of their size on the MTEB Retrieval leaderboard, with the largest model, arctic-embed-l outperforming closed source embedding models such as Cohere's embed-v3 and Open AI's text-embed-3-large. In addition to the details of our training recipe, we have provided several informative ablation studies, which we believe are the cause of our model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models Merrick, Luke Xu, Danmei Nuti, Gaurav Campos, Daniel Computation and Language Artificial Intelligence Information Retrieval This report describes the training dataset creation and recipe behind the family of \texttt{arctic-embed} text embedding models (a set of five models ranging from 22 to 334 million parameters with weights open-sourced under an Apache-2 license). At the time of their release, each model achieved state-of-the-art retrieval accuracy for models of their size on the MTEB Retrieval leaderboard, with the largest model, arctic-embed-l outperforming closed source embedding models such as Cohere's embed-v3 and Open AI's text-embed-3-large. In addition to the details of our training recipe, we have provided several informative ablation studies, which we believe are the cause of our model performance. |
| title | Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2405.05374 |