perceptiveshawty/RankCSE
Implementation of "RankCSE: Unsupervised Sentence Representation Learning via Learning to Rank" (ACL 2023)
This project helps researchers in natural language processing (NLP) to create better text embeddings. It takes large text datasets, like Wikipedia articles, and processes them to generate numerical representations of sentences. These representations can then be used in various downstream NLP tasks to measure how similar different sentences are. This is primarily for NLP researchers and machine learning engineers looking to advance sentence understanding.
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Use this if you are an NLP researcher and need to train models to understand semantic similarity between sentences without relying on labeled data for training.
Not ideal if you are not familiar with training deep learning models or do not have access to computational resources for large-scale text processing.
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Language
Python
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MIT
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Last pushed
Mar 12, 2024
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