Description

I am wondering when Assessing the Factual Accuracy of Generated Text in https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikifact will be publically available since it's already been 6 months. @bengoodrich

From the code (input_pipeline.py) I can see that the ParallelTextInputPipeline automatically generates the SEQUENCE_START and SEQUENCE_END tokens (which means that the input text does not need to have those special tokens).

Does ParallelTextInputPipeline also perform **_padding

When positional encoding is disabled, the embedding scaling is also disabled even though the operations are independent:

https://github.com/OpenNMT/OpenNMT-py/blob/1.0.0/onmt/modules/embeddings.py#L48

In consequence, Transformer models with relative position representations do not follow the reference implementation which scales the embedding [by default](https://github.com/tensorflow/tensor

Current documentation in README explains how to install the toolkit and how to run examples. However, I don't think this is enough for users who want to make some changes to the existing recipes or make their own new recipe. In that case, one needs to understand what run.sh does step by step, but I think docs for that are missing at the moment. It would be great if we provide documentation for:

1. Add CI test for building documentations (Do not ignore warnings and add spellcheck).
2. Fix docstrings with incorrect/inconsistent Sphinx format. Currently, such issues are treated as warnings in the docs building.

Hi,
Is it possible to add benchmarks of some models into documentation for comparison purposes ?
Also run time would be helpful. For example 1M iteration takes a weekend with GTX 1080.

• TransformerDecoder.forward: where does self.training come from?
  https://github.com/asyml/texar-pytorch/blob/d17d502b50da1d95cb70435ed21c6603370ce76d/texar/torch/modules/decoders/transformer_decoders.py#L448-L449

• All arguments should say their types explicitly in the docstring. E.g., what is the type of infer_mode? The [method signature](https://texar-pytorch.readthedocs.

Based on this line of code:
https://github.com/ufal/neuralmonkey/blob/master/neuralmonkey/decoders/output_projection.py#L125

Current implementation isn't flexible enough; if we train a "submodel" (e.g. decoder without attention - not containing any ctx_tensors) we cannot use the trained variables to initialize model with attention defined because the size of the dense layer matrix input become

Environment

• Python 3.7.6
• tensorflow==1.14.0

Log

$ python build_vocab.py data/monument_300/data_300.en > data/monument_300/vocab.en
WARNING:tensorflow:From build_vocab.py:44: VocabularyProcessor.__init__ (from tensorflow.contrib.learn.python.learn.preprocessing.text) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tensorfl