trains a ContentDetectorFunction[] based on the examples given.

Details and Options


open allclose all

Basic Examples  (1)

Train a simple entity detector:

Apply the detector on a new text:

Scope  (1)

Train a detector to identify multiple classes:

Apply the detector on new texts:

Ask for particular properties about detected snippets of text:

Options  (4)

PerformanceGoal  (1)

Use PerformanceGoal"Quality" to emphasize the quality of the result:

Use PerformanceGoal"Speed" to emphasize the speed of computation:

ProgressReporting  (1)

By default, progress is reported in a panel:

Use ProgressReportingFalse to avoid displaying the progress panel:

TimeGoal  (1)

The training time can be influenced by several factors, such as the number of examples and classes:

Use TimeGoal to specify a target time for the training:

ValidationSet  (1)

By default, no validation is performed on the detector. Use ValidationSet to provide validation data:

Wolfram Research (2021), TrainTextContentDetector, Wolfram Language function,


Wolfram Research (2021), TrainTextContentDetector, Wolfram Language function,


Wolfram Language. 2021. "TrainTextContentDetector." Wolfram Language & System Documentation Center. Wolfram Research.


Wolfram Language. (2021). TrainTextContentDetector. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_traintextcontentdetector, author="Wolfram Research", title="{TrainTextContentDetector}", year="2021", howpublished="\url{}", note=[Accessed: 17-June-2024 ]}


@online{reference.wolfram_2024_traintextcontentdetector, organization={Wolfram Research}, title={TrainTextContentDetector}, year={2021}, url={}, note=[Accessed: 17-June-2024 ]}