7/12/2023 0 Comments Decipher text ratings![]() ![]() The sentence is not preprocessed in any way. Each example is a sentence representing the movie review and a corresponding label. Let's take a moment to understand the format of the data. Test_examples, test_labels = tfds.as_numpy(test_data) Train_examples, train_labels = tfds.as_numpy(train_data) The following code downloads the IMDB dataset to your machine (or the colab runtime): train_data, test_data = tfds.load(name="imdb_reviews", split=, The IMDB dataset is available on TensorFlow datasets. Print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE") Print("Eager mode: ", tf.executing_eagerly()) Here you can find more expressive or performant models that you could use to generate the text embedding. ![]() For a more advanced text classification tutorial using tf.keras, see the MLCC Text Classification Guide. This notebook uses tf.keras, a high-level API to build and train models in TensorFlow, and TensorFlow Hub, a library and platform for transfer learning. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews. These are split into 25,000 reviews for training and 25,000 reviews for testing. We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. This is an example of binary-or two-class-classification, an important and widely applicable kind of machine learning problem. This notebook classifies movie reviews as positive or negative using the text of the review.
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