When you use a built-in model, you can select a vector embedding technique to create a
vector embedding for input text. You can choose a technique based on the pre-trained model
you want to use to convert the text to a vector.
A vector embedding represents the text as an array of numbers. Each element in the array
represents a different dimension of the text. To create vector embeddings, select an
input column for embedding and then select one of the following vector embedding
techniques:
Word embedding
Convert each word to a vector using the Word2Vec Gigaword 5th Edition model with
300 dimensions (word2vec_giga_300). Useful for text classification and sentiment
analysis.
BERT embedding
Convert each sentence to a vector using the Smaller BERT Embedding
(L-2_H-768_A-12) model with 768 dimensions (small_bert_L2_768). Useful for text
classification and semantic search.
The built-in vector embedding
techniques can process only English text.
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