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from __future__ import unicode_literals, print_function
import random
import plac
import pickle
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding
"""
Script to train a custom Named Entity Recognizer with Spacy.
import nltk
from nltk.tag.stanford import StanfordNERTagger
"""
Named Entity tagging in Python with NLTK and the Stanford NER tagger
"""
PATH_TO_JAR='/Users/christina/Projects/stanford_nlp/stanford-ner/stanford-ner.jar'
PATH_TO_MODEL = '/Users/christina/Projects/stanford_nlp/stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz'
<html>
<head>
<script src="https://d3js.org/d3.v4.min.js"></script>
<style>
label {
font: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
</style>
import csv
from requests_html import HTMLSession
class FashionArticleScraper:
def __init__(self, csv_file_path):
self.csv_file_path = csv_file_path
self.base_url = 'https://www.nytimes.com/section/fashion'
self.headers = {'User-Agent': 'LVNGDBot 1.0'}
self.process_fashion_articles()
print('hello world!')
UPDATE graffiti_locations
SET the_geom = ST_Transform(
ST_SetSRID(
ST_MakePoint(x * 0.3048006096012192, y * 0.3048006096012192),
32118),
4326
)