cmasch/word-embeddings-from-scratch
Creating word embeddings from scratch and visualize them on TensorBoard. Using trained embeddings in Keras.
This project helps natural language processing practitioners create custom word embeddings from their own text documents. You input your collection of text files, and it outputs a visual representation of how words relate to each other, alongside trained word embedding weights. This is useful for data scientists or NLP engineers who need to understand semantic relationships in their specific textual data.
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Use this if you need to generate numerical representations of words from your unique text dataset for tasks like sentiment analysis, text classification, or semantic search.
Not ideal if you are looking for a plug-and-play solution that doesn't require setting up a development environment or running Python code.
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Last pushed
Feb 18, 2020
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