Change the name to model maker.
PiperOrigin-RevId: 298399535 Change-Id: I47d4c6be1a4eabdad72dd9e4cc0ce67adb2a7b51
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@ -328,5 +328,5 @@ images for each of the new labels you wish to train.
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Learn how to perform transfer learning in the
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<a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">Recognize
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flowers with TensorFlow</a> codelab, or with the [model customization toolkit]
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(https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/image_classification.ipynb).
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flowers with TensorFlow</a> codelab, or with the
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[model maker toolkit](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/image_classification.ipynb).
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@ -93,7 +93,7 @@ Performance benchmark numbers are generated with the tool
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## Use your training dataset
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Follow this
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[tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/text_classification.ipynb)
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[tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/text_classification.ipynb)
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to apply the same technique used here to train a text classification model using
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your own datasets. With the right dataset, you can create a model for use cases
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such as document categorization or toxic comments detection.
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