FraLotito/pytorch-continuous-bag-of-words
The Continuous Bag-of-Words model (CBOW) is frequently used in NLP deep learning. It's a model that tries to predict words given the context of a few words before and a few words after the target word.
This helps natural language processing (NLP) practitioners quickly create numerical representations of words, known as word embeddings. You provide text data, and it outputs these embeddings which can then be used to improve the performance of other, more complex text analysis models. It's designed for data scientists and NLP engineers who build language-aware applications.
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Use this if you need to generate high-quality word embeddings from a text corpus as a pre-training step for other deep learning NLP models.
Not ideal if you need a sequential language model for tasks like text generation or next-word prediction, as this model focuses on context-based word prediction rather than sequential probability.
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Jun 21, 2020
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