Sentence Embedding Model
A Sentence Embedding Model is a text-item embedding model for sentences that can be referenced by a sentence embedding encoder.
- AKA: Sentence Encoding Model, Sentence Representation Model.
- Context:
- It can (typically) transform input sentences into numerical vectors in a high-dimensional space.
- It can (typically) preserve semantic relationships between sentences in the embedding space.
- It can (typically) capture syntactic structures and grammatical patterns.
- It can (typically) maintain context sensitivity across different language domains.
- It can (often) be based on neural network architectures.
- It can (often) be trained on a Large Corpus of Text.
- It can (often) implement self-attention mechanisms for context understanding.
- It can (often) support transfer learning for downstream tasks.
- It can (often) utilize pre-trained weights from language models.
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- It can range from being a Pure Sentence Embedding Model to being a Contextually-Informed Sentence Embedding Model, depending on its context sensitivity.
- It can range from being a Domain-Specific Sentence Embedding Model to being a Universal Sentence Embedding Model, depending on its application scope.
- It can range from being a Simple Averaging Model to being a Complex Transformer Model, depending on its architectural complexity.
- It can range from being a Single Language Model to being a Multilingual Sentence Model, depending on its language coverage.
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- It can utilize techniques such as Word Embedding Aggregation, Sequence Modeling, and Attention Mechanisms.
- It can incorporate positional encodings for word order preservation.
- It can implement cross-attention layers for context processing.
- It can be used by a Sentence Embedding System.
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- Examples:
- Transformer-Based Models, such as:
- S-BERT, which generates context-sensitive embeddings for each word in a sentence and can be adapted to generate sentence embeddings.
- BERT-Based Sentence Models for contextual representation.
- RoBERTa-Based Sentence Models for robust encoding.
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- Traditional Architecture Models, such as:
- GloVe (Global Vectors for Word Representation) combined with average pooling, where word embeddings are averaged to produce a sentence embedding.
- Word2Vec-Based Sentence Models with compositional functions.
- FastText-Based Sentence Models using subword information.
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- Specialized Purpose Models, such as:
- Cross-Lingual Sentence Models for multilingual representation.
- Legal Text Sentence Models for legal domain applications.
- Scientific Text Models for research documents.
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- Experimental Architecture Models, such as:
- Transformer-Based Models, such as:
- Counter-Examples:
- Word Embedding Model, which focuses on generating embeddings for individual words rather than whole sentences.
- Document Embedding Model, which is designed to generate embeddings for entire documents, which may contain multiple sentences or paragraphs.
- Character Embedding Model, which operates at the character level rather than sentence level.
- Image Embedding Model, which generates embeddings for visual content rather than text.
- See: Vector Space Model, Semantic Analysis, Word Embedding, Document Embedding, Neural Language Model, Transformer Architecture, Attention Mechanism, Transfer Learning System.