【工具篇7】spaCy安装、使用教程，快来get！

[Alice_恒源云]

spaCy 是一个自然语言处理库，包括分词、词性标注、词干化、命名实体识别、名词短语提取等功能～

接下来，将告诉你如何在平台快捷安装以及使用～

【安装】

# 安装 spaCy 3 For CUDA 11.2，根据镜像 CUDA 版本替换 [] 内版本
pip install spacy[cuda112]==3.0.6

# 安装 spaCy 2 For CUDA 11.2，根据镜像 CUDA 版本替换 [] 内版本
pip install spacy[cuda112]==2.3.5

# 通过 spacy 模块下载模型因为墙可能不可用，可通过下面 pip 安装方式安装
python -m spacy download en_core_web_sm

# 安装 3.0.0 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

# 安装 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-cache

【使用】

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_)

[Alice_恒源云]

本篇教程参考了技术小哥哥编写的文档，如有任何疑问，请在本帖下留言哈

再附上一个文档原址：https://gpushare.com/docs/best_practices/spacy/