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Change the name to model maker.

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PiperOrigin-RevId: 298399535
Change-Id: I47d4c6be1a4eabdad72dd9e4cc0ce67adb2a7b51
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Tian Lin 2020-03-02 11:19:20 -08:00 committed by TensorFlower Gardener
parent c8bafc52ac
commit a190eddea4
2 changed files with 3 additions and 3 deletions
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4
tensorflow/lite/g3doc/models/image_classification/overview.md
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@ -328,5 +328,5 @@ images for each of the new labels you wish to train.
Learn how to perform transfer learning in the Learn how to perform transfer learning in the
<a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">Recognize <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">Recognize
flowers with TensorFlow</a> codelab, or with the [model customization toolkit] flowers with TensorFlow</a> codelab, or with the
(https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/image_classification.ipynb). [model maker toolkit](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/image_classification.ipynb).

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tensorflow/lite/g3doc/models/text_classification/overview.md
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@ -93,7 +93,7 @@ Performance benchmark numbers are generated with the tool
## Use your training dataset ## Use your training dataset
Follow this Follow this
[tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/text_classification.ipynb) [tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/text_classification.ipynb)
to apply the same technique used here to train a text classification model using to apply the same technique used here to train a text classification model using
your own datasets. With the right dataset, you can create a model for use cases your own datasets. With the right dataset, you can create a model for use cases
such as document categorization or toxic comments detection. such as document categorization or toxic comments detection.