Andreas Klintberg klintan

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

repo: https://github.com/tamuhey/tokenizations

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

Node: Graph data records
Relationship: Connect nodes (has direction and a type)
Property: Stores data in key-value pair in nodes and relationships
Label: Groups nodes and relationships (optional)

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

Experimental Generation of Interpersonal Closeness

Instructions to Subjects Included With Task Slips Packet

This is a study of interpersonal closeness, and your task, which we think will be quite enjoyable, is simply to get close to your partner. We believe that the best way for you to get close to your partner is for you to share with them and for them to share with you. Of course, when we advise you about getting close to your partner, we are giving advice regarding your behavior in this demonstration only, we are not advising you about your behavior outside of this demonstration.

In order to help you get close we've arranged for the two of you to engage in a kind of sharing game. You're sharing time will be for about one hour, after which time we ask you to fill out a questionnaire concerning your experience of getting close to your partner.

You have been given three sets of slips. Each slip has a question or a task written on it. As soon as you both finish reading these instructions, you should

	#!/usr/bin/env python
	# PointCloud2 color cube
	# https://answers.ros.org/question/289576/understanding-the-bytes-in-a-pcl2-message/
	import rospy
	import struct

	from sensor_msgs import point_cloud2
	from sensor_msgs.msg import PointCloud2, PointField
	from std_msgs.msg import Header

	import nltk

	from nltk.tokenize.treebank import TreebankWordTokenizer

	class TreebankSpanTokenizer(TreebankWordTokenizer):

	def __init__(self):
	self._word_tokenizer = TreebankWordTokenizer()

	def span_tokenize(self, text):

	# Keras==1.0.6
	from keras.models import Sequential
	import numpy as np
	from keras.layers.recurrent import LSTM
	from keras.layers.core import TimeDistributedDense, Activation
	from keras.preprocessing.sequence import pad_sequences
	from keras.layers.embeddings import Embedding
	from sklearn.cross_validation import train_test_split
	from keras.layers import Merge
	from keras.backend import tf

	# Copyright (C) 2016 Martina Pugliese

	from boto3 import resource
	from boto3.dynamodb.conditions import Key

	# The boto3 dynamoDB resource
	dynamodb_resource = resource('dynamodb')


	def get_table_metadata(table_name):

	import numpy as np
	import random


	class Node:
	def __init__(self,t,L,R,D,S,V,M,X):
	self.t=t
	self.L=L
	self.R=R
	self.D=D

	from pyspark import SparkConf, SparkContext
	from sklearn.datasets import make_classification
	from sklearn.ensemble import ExtraTreesClassifier
	import pandas as pd
	import numpy as np

	conf = (SparkConf()
	.setMaster("local[*]")
	.setAppName("My app")
	.set("spark.executor.memory", "1g"))