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Overview 

Text embeddings are numerical representations of text data in a continuous vector space. They are used in natural language processing (NLP) and machine learning tasks to convert words, sentences, or entire documents into dense vectors, which can then be processed and analyzed using mathematical operations, capturing semantic meanings and relationships within the text.

Text embedding-gecko

The textembedding-gecko model accepts a maximum of 3,072 input tokens and outputs 768-dimensional vector embeddings.

Use cases

This representation can be used to solve common NLP tasks, such as:

• Semantic search: Search text ranked by semantic similarity.
• Recommendation: Return items with text attributes similar to the given text.
• Classification: Return the class of items whose text attributes are similar to the given text.
• Clustering: Cluster items whose text attributes are similar to the given text.
• Outlier Detection: Return items where text attributes are least related to the given text.

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