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from evalmodel import test_eval_model
dico, df_metrics = test_eval_model("ner_models/mytrainedmodel", TEST_DATA)

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from __future__ import unicode_literals, print_function
import re
import spacy
import pandas as pd
from spacy import displacy
import os
from collections import defaultdict
import itertools
import plac
import random

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#import libraries

from bs4 import BeautifulSoup
import requests
import re
import pandas as pd
import json

class ArticleTable(object):
    """From medium page with listing of articles get links and then extract info from each article"""

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# import custom modules
import html_file_parsing as hfp 

# define listing sources 
Tech_Topics=[
    'https://medium.com/topic/artificial-intelligence',
    'https://medium.com/topic/blockchain',
    'https://medium.com/topic/cryptocurrency',
    'https://medium.com/topic/data-science',
    'https://medium.com/topic/machine-learning',

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import spacy
from spacy import displacy

# install spacy model
#! pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.2.0/en_core_web_md-2.2.0.tar.gz
nlp = spacy.load("en_core_web_md")
doc = nlp("I think Barack Obama met founder of Facebook at occasion of a release of a new NLP algorithm.")

displacy.render(doc, style="dep") # (1)
displacy.render(doc, style="ent") # (2)