pdrm83/sent2vec
How to encode sentences in a high-dimensional vector space, a.k.a., sentence embedding.
This tool helps data scientists and NLP practitioners quickly convert sentences into numerical representations, called embeddings. You input a list of sentences, and it outputs a list of corresponding numerical vectors. These vectors capture the meaning of the sentences, which is crucial for tasks like sentiment analysis or summarization.
135 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to transform text into a machine-understandable format quickly and flexibly for various natural language processing applications.
Not ideal if you are not comfortable working with Python libraries or require a pre-built application rather than a programming tool.
Stars
135
Forks
12
Language
Python
License
MIT
Category
Last pushed
Jun 30, 2022
Commits (30d)
0
Dependencies
6
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