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Self-supervised Sentence Embeddings

Self-supervised sentence embeddings train a neural encoder to map sentences into a dense vector space without requiring manually labeled pairs. By constructing positive examples automatically — for instance by passing the same sentence through dropout twice — and using contrastive objectives, the model learns semantically rich representations that transfer well to similarity, retrieval, and classification tasks.

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Self-supervised Learning for Sentence Embeddings

Rekod kaedah taksonomik · ml-model / deep-learning

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