Text Embeddings by Weakly-Supervised Contrastive Pre-training

Meet E5 — simple text vectors that make search and sorting better

E5 are a new family of text embeddings you can use right away for search, grouping, or classifying short and long text.
It was trained using a big but softly labeled set of text pairs called CCPairs, so the model learns what bits of text are similar, even when no strict labels exist.
You can drop it into apps for quick document retrieval, clustering or filters, and it often works without extra tuning.
In tests across many tasks it even beats older, traditional search methods in zero-shot settings — that means it finds relevant stuff with no task-specific training.
Results were strong both when used straight away, and when fine-tuned further; the model scales well, but stays simple to use.
If you build search or sorting for text, this is one tool to try — easy to add, fast to run, and surprisingly good, even when you haven't prepared special training data.

Read article comprehensive review in Paperium.net:
Text Embeddings by Weakly-Supervised Contrastive Pre-training

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