The adventage of tokenizer like that is lack of duplicates in vocab file (more on that later). It's highly modified own python implementation of that tokenizer. Standard tokenizer is based on moses-smt one. V0.1 includes only standard (own, first version) tokenizer. Standard vs BPE/WPM-like (subword) tokenization, embedded detokenizer As a result returned values will be saved in model logs and plot in TensorBoard. key - lowercase ascii letters only plus underscoreįunction is called on every evaluation.To add custom values, modify custom_summary function inside setup/custom_summary.py.ĭata object is a list of tuples, where tuple contains: TensorBoard will plot those values in a separate graphs. It is possible to add custom values logged into model logs. Version 0.3 introduces epoch-based training including custom (epoch-based as well) decaying scheme - refer to preprocessing in setup/settings.py for more detailed explanation and example (enabled by default). Run setup/prepare_data.py - a new folder called "data" will be created with prepared training data We have provided some sample data for those who just want to do a quick test drive. Place training data inside "new_data" folder (train.(from|to), tst2013.(from|to), tst2013(from|to)).(optional) Edit text files containing rules in the setup directory.These are a decent starting point for ~4GB of VRAM, you should first start by trying to raise vocab if you can. (optional) edit settings.py to your liking.You also need CUDA Toolkit 8.0 and cuDNN 6.1. $ pip install -r requirements.txt TensorFlow-GPU is one of the requirements.$ git clone -branch v0.1 -recursive (for a version featured in Sentdex tutorial) There are multiple changes after last part of tutorial. If you want to use exactly what's in tutorial made by Sentdex, use v0.1 tag. Python 3.4 and 3.5 is likely to work in Linux, but you will eventually hit encoding errors with 3.5 or lower in a Windows environment. It is highly recommended that you use Python 3.6+. Doing so allowed us also to fix some bug before official patch as well as do couple of necessary changes. We had to make a change in our code allowing the use of a stable TensorFlow (1.4) version. The code is built on top of NMT but because of lack of available interfaces, some things are "hacked", and parts of the code had to be copied into that project (and will have to be maintained to follow changes in NMT). Main purpose of that project is to make an NMT chatbot, but it's fully compatible with NMT and still can be used for sentence translations between two languages. Includes BPE/WPM-like tokenizator (own implementation). Nmt-chatbot is the implementation of chatbot using NMT - Neural Machine Translation (seq2seq). More detailed information about training a model.Standard vs BPE/WPM-like (subword) tokenization, embedded detokenizer.
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