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AllenNLP: A Deep Semantic Natural Language Processing Platform
TLDR
AllenNLP is described, a library for applying deep learning methods to NLP research that addresses issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions.
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Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
TLDR
This paper shows how the predicted effects of actions in the context of a paragraph can be improved in two ways: by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and by biasing reading with preferences from large-scale corpora.
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Data Science from Scratch: First Principles with Python
TLDR
This book will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist.
Data science from scratch
This is a first-principles-based, practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills. The book covers: The…
Writing Code for NLP Research
TLDR
This tutorial aims to share best practices for writing code for NLP research, drawing on the instructors' experience designing the recently-released AllenNLP toolkit, a PyTorch-based library for deep learning NLPResearch.
A Deep Semantic Natural Language Processing Platform
TLDR
AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily and provides a flexible data API that handles intelligent batching and padding, and a modular and extensible experiment framework that makes doing good science easy.
RichJackson/pytorch-transformers: supporting ablation paper v3
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