Cryptocurrency

A cryptocurrency forked from Chia and uses the Proof of Space and Time consensus algorithm

SHIBgreen is a cryptocurrency forked from Chia and uses the Proof of Space and Time consensus algorithm Discord Channel: https://discord.gg/PCyTa4CNx4 Website: https://shibgreen.com (coming soon) Initial 1.0.0 release!Per usual should’nt have any issues.Full node port: 7442 EARLY ADOPTERS GET MORE SHIBGREEN!!!!The way SHIBgreen was designed was to get alot of coins farmed early on and then significantly drop very early on in the blockchains life cycle.At height 168192 the block rewards will drop from 100,000 XSHIB to 20,000 XSHIB.Early adopters get […]

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Sphinx

Type hints support for the Sphinx autodoc extension

This extension allows you to use Python 3 annotations for documenting acceptable argument types and return value types of functions. This allows you to use type hints in a very natural fashion, allowing you to migrate from this: def format_unit(value, unit): “”” Formats the given value as a human readable string using the given units. :param float|int value: a numeric value :param str unit: the unit for the value (kg, m, etc.) :rtype: str “”” return ‘{} {}’.format(value, unit) to […]

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Documentation

Main repository for the Sphinx documentation builder

Sphinx is a tool that makes it easy to create intelligent and beautiful documentation for Python projects (or other documents consisting of multiple reStructuredText sources), written by Georg Brandl. It was originally created for the new Python documentation, and has excellent facilities for Python project documentation, but C/C++ is supported as well, and more languages are planned. Sphinx uses reStructuredText as its    

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 94: Notes from WMT 2021

After the notes from EMNLP 2021, here is also an unsorted list of some observations from the Conference on Machine Translation. Facebook AI won in many translation directions (not at all in all of them) in the news task with a multilingual system. At the panel discussion about MT evaluation, Herman Nay expressed a controversial opinion: it does not matter what metric we use, the history of MT would be the same with any metric (that at least slightly correlates […]

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Python

Building a Neural Network & Making Predictions With Python AI

If you’re just starting out in the artificial intelligence (AI) world, then Python is a great language to learn since most of the tools are built using it. Deep learning is a technique used to make predictions using data, and it heavily relies on neural networks. This course will show you how to build a neural network from scratch. In a production setting, you would use a deep learning framework like TensorFlow or PyTorch instead of building your own neural […]

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Facebook NLP Research
NLP

Findings of the WMT 2021 Shared Task on Large-Scale Multilingual Machine Translation

November 8, 2021 By: Guillaume Wenzek, Vishrav Chaudhary, Angela Fan, Sahir Gomez, Naman Goyal, Somya Jain, Douwe Kiela, Tristan Thrush, Francisco Guzmán Abstract We present the results of the first task on Large-Scale Multilingual Machine Translation. The task consists on the many-to-many evaluation of a single model across a variety of source and target languages. This year, the task consisted on three different settings: (i) SMALLTASK1 (Central/South-Eastern European Languages), (ii) the SMALL-TASK2 (South East Asian Languages), and (iii) FULL-TASK (all […]

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Facebook NLP Research
NLP

Findings of the WMT 2021 Shared Task on Quality Estimation

November 8, 2021 By: Lucia Specia, Frédéric Blain, Marina Fomicheva, Chrysoula Zerva, Zhenhao Li, Vishrav Chaudhary, André F. T. Martins Abstract We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additions: (i) prediction for unseen languages, i.e. zero-shot settings, and (ii) prediction of sentences with catastrophic errors. […]

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