"examples/academic_paper_scripts/detxoify_lm/finetune_gpt.py" did not exist on "8fae1cdd207e2d8d72d5b8510035229544645b17"
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Asim Shankar authored
- Use the object-oriented tf.layers API instead of the functional one. The object-oriented API is particularly useful when using the model with eager execution. - Update unittest to train, evaluate, and predict using the model. - Add a micro-benchmark for measuring step-time. The parameters (batch_size, num_steps etc.) have NOT been tuned, the purpose with this code is mostly to illustrate how model benchmarks may be written. These changes are made as a step towards consolidating model definitions for different TensorFlow features (like eager execution and support for TPUs in https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/mnist and https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist
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