When working with text data such as text, conversational text, documents, or HTML, you can use the find text filter in Catalog to surface data rows that contain specific keywords.

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How the find text filter works

When you use the find text filter, you are allowed to specify multiple keywords in the expression. Labelbox will then surface data rows that contain those words.

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Character/word limits

To view the minimum & maximum characters/words for the find text filter, visit our limits page.
If multiple words are provided in the expression, Labelbox will surface data rows in this order:
  1. Data rows containing all words
  2. Data rows containing all words except one
  3. Data rows containing all words except two
… and so on until all results containing at least one of the provided words are returned.

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Supported media types

The filter is supported for the following data types:
Media typeLimit
TextFirst 64k characters
Conversational textFirst 64k characters
HTMLFirst 64k characters
DocumentsFirst 64k characters
The limits above indicate the last character in the media file that the filter will reach. For example, with the current limits, if the keyword you are searching for appears after the 64,000th character in the media file, the data row will not appear in the filter results.

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Combine find text with other filters

You can combine the find text filter with other filters in Catalog. Some filters are best used for targeting unstructured data and others are best for targeting structured data. Combine the find text with the following filters to target data rows by structured data:

Metadata

Annotations

Datasets

Projects

Combine find text with the following filters to target data rows by unstructured data:

Similarity search

Natural language search

Find text can be used in conjunction with other filters to surface high-impact data

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Automate data curation with slices

After you populate the Catalog filters, you can save these filters as a slice of data. When you save a filter as a slice, you will not need to populate the same filters over and over again. Also, slices are dynamic, so any new incoming data rows will automatically appear in the relevant slices. Read through the following resources to learn how to take action on the filtered data.

Refine the similarity search

Send filtered data rows to a labeling project as a batch

Add metadata to the filtered data rows