Python text error

[Prashant93]

Hi, I am getting error in 'TEXTS' that 'too many blank lines, and 'missing white space around operator' and also I am not able to run that program. Please help.

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from __future__ import unicode_literals, print_function   import plac import spacy       TEXTS ='Microsoft was founded by Bill Gates and Paul Allen on April 4 1975 to develop and sell BASIC interpreters for the Altair 8800. Since the 1990, it has increasingly diversified from the operating system market and has made a number of corporate acquisitions. ',     @plac.annotations(model=("Model to load (needs parser and NER)", "positional", None, str)) def main(model='en_core_web_sm'):     nlp = spacy.load(model)     print("Loaded model '%s'" % model)     print("Processing %d texts" % len(TEXTS))       for text in TEXTS:         doc = nlp(text)         relations = extract_currency_relations(doc)         for r1, r2 in relations:             print('{:<10}\t{}\t{}'.format(r1.text, r2.ent_type_, r2.text))     def extract_currency_relations(doc):     # merge entities and noun chunks into one token     spans = list(doc.ents) + list(doc.noun_chunks)     for span in spans:         span.merge()       relations = []     for money in filter(lambda w : w.ent_type_ == 'PERSON', doc):         if money.dep_ in ('attr', 'dobj'):             subject = [w for w in money.head.lefts if w.dep_ == 'nsubj']             if subject:                 subject = subject[0]                 relations.append((subject, money))         elif money.dep_ == 'pobj' and money.head.dep_ == 'prep':             relations.append((money.head.head, money))     return relations     if __name__ == '__main__':     plac.call(main)

[ichabod801]

For multi line code use python tags, not icode tasks. I'll fix it for you this time.

Those sound like errors you would get from a style checking program, not Python. How exactly are you running the code?

Why is there a comma after the definition of TEXTS?

[Prashant93]

I am using visual code. Let's say for this example it gives me error:
Loaded model 'en_core_web_sm'
Processing 1 texts

and then it stops and does not gives me anything. Please help

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# pip install spacy # python -m spacy download en_core_web_sm   import spacy   # Load English tokenizer, tagger, parser, NER and word vectors nlp = spacy.load('en_core_web_sm')   # Process whole documents text = (u"When Sebastian Thrun started working on self-driving cars at "         u"Google in 2007, few people outside of the company took him "         u"seriously. “I can tell you very senior CEOs of major American "         u"car companies would shake my hand and turn away because I wasn’t "         u"worth talking to,” said Thrun, now the co-founder and CEO of "         u"online higher education startup Udacity, in an interview with "         u"Recode earlier this week.") doc = nlp(text)   # Find named entities, phrases and concepts for entity in doc.ents:     print(entity.text, entity.label_)   # Determine semantic similarities doc1 = nlp(u"my fries were super gross") doc2 = nlp(u"such disgusting fries") similarity = doc1.similarity(doc2) print(doc1.text, doc2.text, similarity)

[Prashant93]

I tried to work on this code but it is giving me output only for money and not org and person. Any help would be greatly appreciated. Thanks.

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from __future__ import unicode_literals, print_function   import plac import spacy     TEXTS = [     'Net income was $9.4 million compared to the prior year of $2.7 million.',     'Revenue exceeded twelve billion dollars, with a loss of $1b.', ]     @plac.annotations(    model=("Model to load (needs parser and NER)", "positional", None, str)) def main(model='en_core_web_sm'):     nlp = spacy.load(model)     print("Loaded model '%s'" % model)     print("Processing %d texts" % len(TEXTS))       for text in TEXTS:         doc = nlp(text)         relations = extract_currency_relations(doc)         for r1, r2 in relations:             print('{:<10}\t{}\t{}'.format(r1.text, r2.ent_type_, r2.text))     def extract_currency_relations(doc):     # merge entities and noun chunks into one token     spans = list(doc.ents) + list(doc.noun_chunks)     for span in spans:         span.merge()       relations = []     for money in filter(lambda w: w.ent_type_ == 'MONEY', doc):         if money.dep_ in ('attr', 'dobj'):             subject = [w for w in money.head.lefts if w.dep_ == 'nsubj']             if subject:                 subject = subject[0]                 relations.append((subject, money))         elif money.dep_ == 'pobj' and money.head.dep_ == 'prep':             relations.append((money.head.head, money))     return relations       relations = []     for person in filter(lambda w: w.ent_type_ == 'PERSON', doc):         if person.dep_ in ('attr', 'dobj'):             subject = [w for w in person.head.lefts if w.dep_ == 'nsubj']             if subject:                 subject = subject[0]                 relations.append((subject, person))         elif person.dep_ == 'pobj' and person.head.dep_ == 'prep':             relations.append((person.head.head, person))     return relations       relations = []     for org in filter(lambda w: w.ent_type_ == 'ORG', doc):         if org.dep_ in ('attr', 'dobj'):             subject = [w for w in org.head.lefts if w.dep_ == 'nsubj']             if subject:                 subject = subject[0]                 relations.append((subject, org))         elif org.dep_ == 'pobj' and org.head.dep_ == 'prep':             relations.append((org.head.head, org))     return relations     if __name__ == '__main__':     plac.call(main)     main()