text: import json
  import re
  from nltk.tokenize import sent_tokenize, word_tokenize
  import spacy
  
  data = None
  raw_sentences = {}
  sentences = {}
  nlp = spacy.load("en_core_web_trf")
  
  with open("/Users/parvinderjit.singh/Desktop/Harvard/CSCI E-599/Patent_Visualization_Project/text-mining-poc/protein-sequences/test_data.json", "r") as read_file:
      data = json.load(read_file)
  for patent in data:
      raw_sentences[patent["patentid"]] = []
      raw_sentences[patent["patentid"]] += sent_tokenize(patent['abstract'])
      raw_sentences[patent["patentid"]] += sent_tokenize(patent['claims'])
      raw_sentences[patent["patentid"]] += sent_tokenize(patent['description'])
  for k in raw_sentences.keys():
      sentences[k] = []
      for s in raw_sentences[k]:
          if re.search('SEQ ID', s, re.IGNORECASE):
              sentences[k].append(s)
  for k in sentences.keys():
      print(k, len(sentences[k]))