DataLoader class for Text Searcher.

Inherits From: DataLoader

Used in the notebooks

Used in the tutorials

embedder Embedder to generate embedding from raw input image.

dataset Gets the dataset.

Due to performance consideration, we don't return a copy, but the returned self._dataset should never be changed.

embedder_path Gets the path to the TFLite Embedder model file.
metadata Gets the metadata.



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Appends the dataset.

Don't check if embedders from the two data loader are the same in this function. Users are responsible to keep the embedder identical.

data_loader The data loader in which the data will be appended.


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Creates DataLoader for the Text Searcher task.

text_embedder_path Path to the ".tflite" text embedder model. case and L2 norm is thus achieved through TF Lite inference.
l2_normalize Whether to normalize the returned feature vector with L2 norm. Use this option only if the model does not already contain a native L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and L2 norm is thus achieved through TF Lite inference.

DataLoader object created for the Text Searcher task.


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Loads text data from csv file that includes a "header" line with titles.

Users can load text from different csv files one by one. For instance,

# Creates data_loader instance.
data_loader = text_searcher_dataloader.DataLoader.create(tflite_path)

# Loads text, first from `text_path1` and secondly from `text_path2`.
    text_path1, text_column='text', metadata_column='metadata')
    text_path2, text_column='text', metadata_column='metadata')

path Text csv file path to be loaded.
text_column Column name for input text.
metadata_column Column name for user metadata associated with each input text.
delimiter Character used to separate fields.
quotechar Character used to quote fields containing special characters.


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