Difference between revisions of "Continuous Bag-of-Words (CBoW)"
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* [[Bag-of-Words (BoW)]] | * [[Bag-of-Words (BoW)]] | ||
| − | * [[Natural Language Processing (NLP)]] | + | * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] |
* [[Word2Vec]] | * [[Word2Vec]] | ||
* [[Skip-Gram]] | * [[Skip-Gram]] | ||
| − | The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of ( | + | The CBOW model architecture tries to predict the current target word (the center word) based on the source [[context]] words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of ([[context]]_window, target_word) where if we consider a [[context]] window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the [[context]]_window words. [https://towardsdatascience.com/understanding-feature-engineering-part-4-deep-learning-methods-for-text-data-96c44370bbfa A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science] |
https://miro.medium.com/max/542/1*d66FyqIMWtDCtOuJ_GcqAg.png | https://miro.medium.com/max/542/1*d66FyqIMWtDCtOuJ_GcqAg.png | ||
Latest revision as of 20:37, 17 May 2023
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- Bag-of-Words (BoW)
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Word2Vec
- Skip-Gram
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science
