Text and Code Embeddings by Contrastive Pre-Training

Powerful Text and Code Embeddings That Improve Search and Accuracy

Imagine search and code tools that just get better, with no hand-labeled data and lots of plain text to learn from.
Researchers taught models by comparing examples, teaching them to pull similar things together and push different things apart, and the result is compact signatures for words and code that work across many tasks.
These new text and code fingerprints are stronger at finding answers in big piles of text and they also make simple classifiers more accurate than before.
In tests the approach was often much better — sometimes up to about 23% improvement on search tasks, and near 20% for code lookup, while also raising classification scores a few percent.
That means faster, smarter search and easier discovery of code snippets, without needing extra tuning.
It feels like a small change in training but it makes search, answers and code tools more useful, and you might notice better results in apps that use this kind of tech soon, even if you dont see all the behind-the-scenes work.

Read article comprehensive review in Paperium.net:
Text and Code Embeddings by Contrastive Pre-Training

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