qiyuw/PeerCL
EMNLP 2022 "PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings"
This tool helps developers and researchers create highly accurate numerical representations (embeddings) of sentences without needing labeled data. You provide raw text, and it generates an embedding that captures the meaning of the sentence, allowing you to easily compare different sentences for semantic similarity. It's used by machine learning engineers and NLP researchers to improve tasks like information retrieval or text classification.
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Use this if you need to generate high-quality, unsupervised sentence embeddings for natural language processing tasks.
Not ideal if you don't work with machine learning models or require a ready-to-use application rather than a foundational modeling tool.
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Python
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Last pushed
Jul 31, 2023
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