Article source | turbine cloud community (ai/deep learning cloud GPU server address training platform gpushare.com/)

SpaCy is a natural language processing library, including word segmentation, part-of-speech tagging, word stem, named entity recognition, noun phrase extraction and other functions

Next, we will show you how to quickly install and use ~

“Installation”

PIP install spaCy [cuda112]==3.0.6 # Install spaCy 2 For CUDA 11.2, PIP install spacy[CUDa112]==2.3.5 # -m spacy download en_core_web_sm PIP install en_core_web_sm PIP install -m spacy download en_core_web_sm PIP install https://ghproxy.com/https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0. 0-py3-none-any. WHL --no-cache # install 2.3.1 en_core_web_sm PIP install https://ghproxy.com/https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3. 1.tar.gz --no-cacheCopy the code

[use]

import spacy # Load English tokenizer, tagger, parser and NER nlp = spacy.load("en_core_web_sm") # Process whole documents text = ("When Sebastian Thrun started working on self-driving cars at " "Google in 2007, Few people outside of the company took him ""seriously." "I can tell you very senior CEOs of major American" "car Companies would shake my hand and turn away because I wasn't ""worth talking to,"  said Thrun, in an interview with Recode earlier " "this week.") 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 for entity in doc.ents: print(entity.text, entity.label_)Copy the code