Spacy 目前支持jieba中文分词，尝试使用Spacy导入预训练的wordvector，最后测试基于wordvector的词语相似度。
不同于早期版本，Spacy 2.0对词向量等模型的load和save接口做了修改，这里使用Spacy 2.0进行测试，用了官方的一段代码：
loadvector.py :
#!/usr/bin/env python
# coding: utf8
"""Load vectors for a language trained using fastText
https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals
import plac
import numpy
import spacy
from spacy.language import Language
@plac.annotations(
vectors_loc=("Path to .vec file", "positional", None, str),
lang=("Optional language ID. If not set, blank Language() will be used.",
"positional", None, str))
def main(vectors_loc, lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
nr_row, nr_dim = header.split()
print(nr_row, nr_dim)
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
# test the vectors and similarity
text = '您好,你好'
doc = nlp(text)
print(text, doc[0].similarity(doc[2]))
nlp.to_disk("./zh_model")
if __name__ == '__main__':
plac.call(main)
运行代码，传入Word2vector路径，这里的词向量使用fasttext训练的非二进制文件：
17484 100
的 -0.093497 -0.16838 -0.31183 0.18158 0.14234 0.047932 -0.17727 -0.11675 0.037068 -0.090361 -0.24306 0.096267 -0.17542 0.17559 -0.012545 0.1336 0.13552 -0.10716 0.10519 -0.076989 -0.11632 -0.14894
-0.099211 -0.068264 -0.16019 -0.20795 0.10994 -0.19069 -0.070186 -0.10722 0.056536 0.037165 -0.16839 -0.2232 -0.42118 -0.25819 0.086529 0.18487 -0.044813 0.07809 0.15395 0.096284 -0.0054108 0.0963
5 0.045701 0.11826 0.02093 -0.061605 -0.069395 0.098948 -0.093462 -0.10125 -0.14047 0.12453 -0.1935 0.12049 -0.25669 0.08099 -0.086279 0.23138 -0.097905 -0.19973 0.34899 0.11208 -0.025583 -0.11361
-0.23792 -0.32146 0.25924 -0.013813 -0.2467 0.039815 -0.073362 -0.31727 -0.050605 0.075048 -0.25274 0.18276 0.097259 0.17918 -0.052097 -0.24945 -0.034484 -0.093092 0.095478 -0.017527 0.03188 0.0355
25 -0.26906 0.016382 -0.22175 -0.061337 -0.15386 -0.12769 0.115 0.082991 -0.055711 0.033301 0.0084445 0.1597
运行代码，指定中文语言（zh）
python loadvector.py ./tests/data/mobile.vec zh
这时候会在zh_model下生成模型。
zh_model
├── meta.json
├── tokenizer
└── vocab
├── key2row
├── lexemes.bin
├── strings.json
└── vectors
之后就可以调用spacy.load("./zh_model")使用模型。
小结：
在调用spacy.load("./zh_model")时当前版本会抛出：
ValueError: No valid 'lang' setting found in model meta.json
原因是spacy中文支持没有实现meta.json信息的写入，仿照其他语言实现该部分，并向spacy提了issue和pull request，想不到第二天就被merge。最后引用spacy开发者指南上的一句话:
You don't have to be an NLP expert or Python pro to contribute, and we're happy to help you get started.
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