@@ -23,7 +23,7 @@ All tasks produce TFRecord files of `tensorflow.Example` protocol buffers.
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To add a new problem, subclass
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[ ` Problem ` ] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/problem.py )
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and register it with ` @registry.register_problem ` . See
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- [ ` WMTEnDeTokens8k ` ] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wmt .py )
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+ [ ` TranslateEndeWmt8k ` ] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/translate_ende .py )
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for an example.
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` Problem ` s support data generation, training, and decoding.
@@ -37,7 +37,7 @@ for training/decoding, e.g. a vocabulary file.
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A particularly easy way to implement ` Problem.generate_data ` for your dataset is
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to create 2 Python generators, one for the training data and another for the
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dev data, and pass them to ` generator_utils.generate_dataset_and_shuffle ` . See
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- [ ` WMTEnDeTokens8k .generate_data` ] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wmt .py )
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+ [ ` TranslateEndeWmt8k .generate_data` ] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/translate_ende .py )
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for an example of usage.
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The generators should yield dictionaries with string keys and values being lists
@@ -66,5 +66,5 @@ Some examples:
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* [ Algorithmic problems] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/algorithmic.py )
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and their [ unit tests] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/algorithmic_test.py )
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- * [ WMT problems] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wmt .py )
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+ * [ WMT En-De problems] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/translate_ende .py )
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and their [ unit tests] ( https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wmt_test.py )
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