Sentence Embedding Vector
(Redirected from sentence embedding item)
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].BERTmodel 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.