jcassiojr

mainly record pytorch implementations for NLI and similarity computing

REPOSITORY	REFERENCE
baidu/SimNet	SEVERAL
NTSC-Community/awaresome-neural-models-for-semantic-match	SEVERAL
lanwuwei/SPM_toolkit: ①DecAtt ②ESIM ③PWIM ④SSE	Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
MatchZoo (Keras Implementation): ①DSSM ②CDSSM ③ARC-I ④ARC-II ⑤MV-LSTM ⑥DRMM ⑦K-NRM ⑧CONV-KNRM ⑨DRMM-TKS ⑩BiMPM ...	MatchZoo: A Toolkit for Deep Text Matching ...
[likejazz/Si

Intro

I don't have the usual programmer's education. I studied maths, and then dropped out of that, and am mostly self-taught. So, there are some parts of programming I always saw wearily, thinking to myself that I really should go to school to learn them. One remarkable such area is parsing and implementing languages.

Well... sure, school is always a good idea, but this is not that hard. In this article I will explain how to go from nothing to a functioning, extensible language, using Python and PyParsing. If you are as scared of grammars, parsers and all that jazz as I used to be, come along, it's pretty simple,

Grammar (part I)

What is a grammar? It's what defines what is and what is not valid in our language, and it's the hardest part to get right, mostly because it's all about making decisions.

	import gym
	import numpy as np
	import matplotlib.pyplot as plt
	import copy

	#Hyperparameters
	NUM_EPISODES = 10000
	LEARNING_RATE = 0.000025
	GAMMA = 0.99

	import gym
	import numpy as np
	import sklearn.pipeline
	from sklearn.kernel_approximation import RBFSampler
	import matplotlib.pyplot as plt
	import copy

	#Hyperparameters
	NUM_EPISODES = 10000
	LEARNING_RATE = 0.000025

	"""
	Solving FrozenLake8x8 environment using Policy iteration.
	Author : Moustafa Alzantot (malzantot@ucla.edu)
	"""
	import numpy as np
	import gym
	from gym import wrappers


	def run_episode(env, policy, gamma = 1.0, render = False):