One, which is smaller than the above but takes 38ms for each embedding.Īfter this step, you should have a standalone TFLite searcher model (e.g. Which is optimized for on-device inference. a TFLite text embedder model, such as the Universal Sentence Encoder.Input text processing, including in-graph or out-of-graphīefore using the TextSearcher API, an index needs to be built based on theĬustom corpus of text to search into. Takes a single string as input, performs embedding extraction and Use the Task Library TextSearcher API to deploy your custom text searcher into This alsoĮnables working with larger (100k+ items) corpuses. New items can be added simply re-building the index. Semantic meaning of the query, followed by similarity search in a predefined,Įxpanding the number of items that can be recognized doesn't require re-training It worksīy embedding the search query into a high-dimensional vector representing the Text search allows searching for semantically similar text in a corpus.
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