Rooba
/
spaCy
mirror of https://github.com/explosion/spaCy.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Improve landing example [ci skip]

This commit is contained in:
Ines Montani 2019-03-22 19:02:15 +01:00
parent a841324034
commit dcd6e06c47
1 changed files with 12 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
website/src/widgets/landing.js
View File

@ -23,32 +23,29 @@ import Link from '../components/link'
import BenchmarksChoi from 'usage/_benchmarks-choi.md' import BenchmarksChoi from 'usage/_benchmarks-choi.md'
const CODE_EXAMPLE = `# pip install spacy const CODE_EXAMPLE = `# pip install spacy
# python -m spacy download en_core_web_md # python -m spacy download en_core_web_sm
import spacy import spacy
# Load English tokenizer, tagger, parser, NER and word vectors # Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load("en_core_web_md") nlp = spacy.load("en_core_web_sm")
# Process whole documents # Process whole documents
text = (u"When Sebastian Thrun started working on self-driving cars at " text = ("When Sebastian Thrun started working on self-driving cars at "
u"Google in 2007, few people outside of the company took him " "Google in 2007, few people outside of the company took him "
u"seriously. “I can tell you very senior CEOs of major American " "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 " "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 " "worth talking to,” said Thrun, in an interview with Recode earlier "
u"online higher education startup Udacity, in an interview with " "this week.")
u"Recode earlier this week.")
doc = nlp(text) doc = nlp(text)
# Analyze syntax
print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
# Find named entities, phrases and concepts # Find named entities, phrases and concepts
for entity in doc.ents: for entity in doc.ents:
print(entity.text, entity.label_) 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)
` `
/** /**