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Mike Jang
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@@ -38,7 +38,7 @@ You can also try out these techniques with the following Jupyter notebook: [Open
As the technology of embedding models has advanced, demand has grown. Users are looking more for powerful and efficient text-embedding models. OpenAI's Ada-003 embeddings offer state-of-the-art performance on a wide range of NLP tasks, including those noted in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) and [MIRACL](https://openai.com/blog/new-embedding-models-and-api-updates).
A notable feature of these models is their multi-lingual support, enabling encoding in over 100 languages, which addresses the needs of applications with diverse language requirements. Impressively, the transition from text-embedding-ada-002 to text-embedding-3-large has observed a significant jump in performance scores (from 31.4% to 54.9% on MIRACL), reflecting substantial advancements.
These models include multilingual support in over 100 languages. The transition from text-embedding-ada-002 to text-embedding-3-large has led to a significant jump in performance scores (from 31.4% to 54.9% on MIRACL).
#### Matryoshka Representation Learning