What are Token Embeddings?
Vector representations of individual tokens, such as words or subwords, used in language models.
More about Token Embeddings:
Token Embeddings are dense vector representations of individual tokens (e.g., words or subwords) in a high-dimensional space. These embeddings capture semantic relationships between tokens and are generated by models like BERT or GPT.
Token embeddings are foundational to tasks like semantic search, dense retrieval, and context-aware generation, where understanding token-level relationships is critical for performance.
Frequently Asked Questions
How are token embeddings generated?
They are generated by neural networks trained on large datasets, capturing semantic and syntactic token relationships.
What applications use token embeddings?
Applications include retrieval augmentation pipelines, knowledge retrieval, and document similarity.
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