Sentence Embedding Vector

A Sentence Embedding Vector is a text-item embedding vector within a sentence embedding space that represents a sentence item.

• Context:
  • It can (typically) be produced by a Sentence Embedding Encoder.
  • It can (typically) represent the semantic meaning of a sentence.
  • It can (typically) be a High-Dimensional Vector.
  • It can support NLP Tasks, such as: sentence similarity, text classification, information retrieval, and question answering.
  • It can be fine-tuned for specific domains or tasks to enhance its relevance and accuracy in representing sentences.
  • ...
• Example(s):
  • sentence_encoder("the quick brown fox jumps over the lazy dog") => [0.321, ..., -0.123].
  • sentence_encoder("the elephant in pajamas") => [0.1884, ..., 0.5273].
  • BERT model encoding of “Paris is the capital of France.” => [0.874, ..., -0.654].
  • ...
• Counter-Example(s):
  • A Word Embedding Vector, which represents individual words rather than full sentences.
  • A Document Embedding Vector, which represents entire documents or paragraphs instead of single sentences.
• See: Bag-of-Words Model, Sentence Similarity.

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