n0obcoder/Skip-Gram-Model-PyTorch
PyTorch implementation of the Word2Vec (Skip-Gram Model) and visualizing the trained embeddings using TSNE
This helps data scientists, researchers, and NLP practitioners understand the relationships between words in a large body of text. You input a collection of text documents, and it outputs a set of numerical representations (embeddings) for each word, which can then be visualized to show how words are semantically related. This is useful for anyone working with textual data who needs to find hidden patterns or similarities between terms.
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Use this if you need to generate numerical word representations from a text corpus to analyze semantic relationships, find similar words, or prepare data for other machine learning tasks.
Not ideal if you're looking for a pre-trained model for common languages or a simple API to use word embeddings directly without needing to train your own.
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Python
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Last pushed
Sep 06, 2020
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