What are different sentence embeddings technical and how it affects the output.

Question

Accepted Answer

Different sentence embeddings techniques include Word2Vec, GloVe, FastText, and BERT, each affecting output based on context and complexity.

Word2Vec: Represents words as vectors based on context, affecting similarity and relationships between words.
GloVe: Global Vectors for Word Representation, considers global word co-occurrence statistics to generate embeddings.
FastText: Enriches Word2Vec by considering subword information, useful for handling out-of-vocabulary words.
BERT: Bidirectional Encoder Representations from Transformers, captures context from both directions, leading to better understanding of sentence semantics.

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