nlp - 使用SpaCy时下载 'models'有什么意义？

Question

这个模型有什么作用？

我看到这个术语在 NLP 和 ML 方面普遍存在，而且似乎没有一个具体的定义。

这些模型在 NLP 和 SpaCy 方面取得了哪些成果？

import spacy
from spacy import displacy

nlp = spacy.load('en_core_web_sm')
doc = nlp(u'This is a sentence.')
displacy.serve(doc, style='dep', options={'compact': True})

Answer 1

spaCy 101 guide有一个关于此的部分 - 请参阅此处:

While some of spaCy's features work independently, others require statistical models to be loaded, which enable spaCy to predict linguistic annotations – for example, whether a word is a verb or a noun. spaCy currently offers statistical models for 8 languages, which can be installed as individual Python modules. Models can differ in size, speed, memory usage, accuracy and the data they include. The model you choose always depends on your use case and the texts you're working with. For a general-purpose use case, the small, default models are always a good start. They typically include the following components:

• Binary weights for the part-of-speech tagger, dependency parser and named entity recognizer to predict those annotations in context.
• Lexical entries in the vocabulary, i.e. words and their context-independent attributes like the shape or spelling.
• Word vectors, i.e. multi-dimensional meaning representations of words that let you determine how similar they are to each other.
• Configuration options, like the language and processing pipeline settings, to put spaCy in the correct state when you load in the model.