Dec 15, 2022 · The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search, and outperforms our previous most capable model, Davinci, at most tasks, while being priced 99.8% lower.

新たなモデルである「 text-embedding-ada-002 」は、テキスト検索とテキスト類似性、およびコード検索のための5つのモデルを置き換え、これまで最も高性能だったモデルである Davinci をほとんどのタスクで上回りながらも、コストが99.8%低くなりました。

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text-embedding-3-small は、当社の新しい高効率な埋め込みモデルであり、 2022年12月 ⁠ にリリースされた前身の text-embedding-ada-002 モデルから大幅なアップグレードを提供します。 より高性能。

Nov 1, 2023 · embeddings with “text-embedding-ada-002” is always a vector of 1536. See: New and improved embedding model. The new embeddings have only 1536 dimensions, one-eighth the size of davinci-001 embeddings, making the new embeddings more cost effective in working with vector databases.

Apr 24, 2023 · ada-002 should do some “contextual” ML (NN with transformers, etc etc) with a lot of quite advanced bells and whistles. Yeah, I see where you’re going with this. But is an embedding approach ideal for this good vs bad objective?

Jan 9, 2023 · The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search, and outperforms our previous most capable model, Davinci, at most tasks, while being priced 99.8% lower.

Jan 25, 2024 · text-embedding-3-small is our new highly efficient embedding model and provides a significant upgrade over its predecessor, the text-embedding-ada-002 model released in December 2022 ⁠. Stronger performance.

Oct 23, 2023 · Does anyone have any experiments or research to show that Ada 002 is superior to BERT? — From my paired t-tests — I used MTEB 2016’s sentence pairs and ran them Ada 002 and the three BERT models to generate their cosine similarities.

Jan 25, 2022 · text-similarity-{ada, babbage, curie, davinci}-001. Clustering, regression, anomaly detection, visualization. Semantic information retrieval over documents. text-search-{ada, babbage, curie, davinci}-{query, doc}-001. Search, context relevance, information retrieval. Find relevant code with a query in natural language.