感知机生成的bin可以直接调用么？

HanLP
#1

感知机训练生成的bin，可以下次python直接调用么？而不是每次调出都要全程再训练一遍？如果可以，怎么修改下面代码？

from pyhanlp import *
import zipfile
import os
from pyhanlp.static import download, remove_file, HANLP_DATA_PATH

def test_data_path():
“”"
获取测试数据路径，位于$root/data/test，根目录由配置文件指定。
:return:
“”"
data_path = os.path.join(HANLP_DATA_PATH, ‘test’)
if not os.path.isdir(data_path):
os.mkdir(data_path)
return data_path

验证是否存在 MSR语料库，如果没有自动下载

def ensure_data(data_name, data_url):
root_path = test_data_path()
dest_path = os.path.join(root_path, data_name)
if os.path.exists(dest_path):
return dest_path

if data_url.endswith('.zip'):
    dest_path += '.zip'
download(data_url, dest_path)
if data_url.endswith('.zip'):
    with zipfile.ZipFile(dest_path, "r") as archive:
        archive.extractall(root_path)
    remove_file(dest_path)
    dest_path = dest_path[:-len('.zip')]
return dest_path

指定 PKU 语料库

PKU98 = ensure_data(“pku98”, “http://file.hankcs.com/corpus/pku98.zip”)
PKU199801 = os.path.join(PKU98, ‘199801.txt’)
PKU199801_TRAIN = os.path.join(PKU98, ‘199801-train.txt’)
PKU199801_TEST = os.path.join(PKU98, ‘199801-test.txt’)
POS_MODEL = os.path.join(PKU98, ‘pos.bin’)
NER_MODEL = os.path.join(PKU98, ‘ner.bin’)

===============================================

以下开始 感知机 命名实体识别

NERTrainer = JClass(‘com.hankcs.hanlp.model.perceptron.NERTrainer’)
PerceptronNERecognizer = JClass(‘com.hankcs.hanlp.model.perceptron.PerceptronNERecognizer’)
PerceptronSegmenter = JClass(‘com.hankcs.hanlp.model.perceptron.PerceptronSegmenter’)
PerceptronPOSTagger = JClass(‘com.hankcs.hanlp.model.perceptron.PerceptronPOSTagger’)
Sentence = JClass(‘com.hankcs.hanlp.corpus.document.sentence.Sentence’)
AbstractLexicalAnalyzer = JClass(‘com.hankcs.hanlp.tokenizer.lexical.AbstractLexicalAnalyzer’)
Utility = JClass(‘com.hankcs.hanlp.model.perceptron.utility.Utility’)

def train(corpus, model):
trainer = NERTrainer()
return PerceptronNERecognizer(trainer.train(corpus, model).getModel())

def test(recognizer):
# 包装了感知机分词器和词性标注器的词法分析器
analyzer = AbstractLexicalAnalyzer(PerceptronSegmenter(), PerceptronPOSTagger(), recognizer)
print(analyzer.analyze(“下雨天地面积水。”))

recognizer = train(PKU199801_TRAIN, NER_MODEL)
test(recognizer)