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Sentence embeddings are distributed representations of sentences intended to be general features to be effectively used as input for deep learning models ...
Apr 15, 2024 · Embeddings are useful because you can calculate how similar two sentences are by converting them both to vectors, and calculating a distance metric.
Missing: Sequential | Show results with:Sequential
This work proposes a novel approach to compute sentence embeddings for semantic similarity that exploits a linear autoencoder for sequences and provides a ...
Oct 22, 2024 · Sentence embedding techniques represent entire sentences and their semantic information as vectors. This helps the machine understand the context, intention, ...
Nov 5, 2023 · In this integration, we leverage Jina Embeddings, a powerful text embedding model, in conjunction with the Hugging Face Transformers library.
Mar 11, 2021 · Semantic similarity search is the task of searching for documents or sentences which contain semantically similar content to a user-submitted ...
For Semantic Textual Similarity (STS), we want to produce embeddings for all texts involved and calculate the similarities between them.
Missing: Sequential | Show results with:Sequential
Duration: 1:03:33
Posted: Apr 4, 2022
Posted: Apr 4, 2022
Missing: Sequential | Show results with:Sequential
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A SICK cure for the evaluation of compositional distributional semantic models. Conference Paper. Full-text available. May 2014.
How to find Sentence Similarity using Transformer Embeddings
bekushal.medium.com › how-to-find-sen...
Sep 15, 2023 · In this blog, we will learn how to use BERT and its close cousin, SBERT, for finding similarity between two sentences.