pabvald/semantic-similarity
Comparison of methods based on pre-trained Word2Vec, GloVe and FastText vectors to measure the semantic similarity between sentence pairs
This project helps developers and researchers evaluate and compare different techniques for determining how similar the meaning is between pairs of sentences. It takes pre-trained word embedding models (like Word2Vec, GloVe, and FastText) and a dataset of sentence pairs, then outputs a quantitative comparison of how well each method measures semantic similarity. This is for anyone building or evaluating natural language processing systems where understanding sentence meaning is crucial.
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Use this if you are a machine learning engineer or NLP researcher comparing state-of-the-art methods for sentence semantic similarity.
Not ideal if you need an out-of-the-box solution for applying semantic similarity to your own data or building a production application.
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AGPL-3.0
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Mar 28, 2023
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