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word2vec.py
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# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""
import os
import time
from typing import List, Union, Dict
import numpy as np
from loguru import logger
from numpy import ndarray
from tqdm import tqdm
from text2vec.utils.get_file import get_file
from text2vec.utils.tokenizer import JiebaTokenizer
pwd_path = os.path.abspath(os.path.dirname(__file__))
default_stopwords_file = os.path.join(pwd_path, 'stopwords.txt')
USER_DATA_DIR = os.path.expanduser('~/.text2vec/datasets/')
def load_stopwords(file_path):
stopwords = set()
if file_path and os.path.exists(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
stopwords.add(line)
return stopwords
class Word2Vec:
"""Pre-trained word2vec embedding"""
model_key_map = {
# 腾讯词向量, 6.78G, 下载不了
'w2v-tencent-chinese': {
'tar_filename': 'Tencent_AILab_ChineseEmbedding.tar.gz',
'url': 'https://ai.tencent.com/ailab/nlp/data/Tencent_AILab_ChineseEmbedding.tar.gz', # dropped
'binary': False,
'untar_filename': 'Tencent_AILab_ChineseEmbedding.txt'},
# 轻量版腾讯词向量，二进制，111MB
'w2v-light-tencent-chinese': {
'tar_filename': 'light_Tencent_AILab_ChineseEmbedding.bin',
'url': 'https://modelscope.cn/models/lili666/text2vec-word2vec-tencent-chinese/resolve/master/light_Tencent_AILab_ChineseEmbedding.bin',
'binary': True,
'untar_filename': 'light_Tencent_AILab_ChineseEmbedding.bin'},
}
def __init__(
self,
model_name_or_path: str = 'w2v-light-tencent-chinese',
w2v_kwargs: Dict = None,
stopwords: List[str] = None,
cache_folder: str = USER_DATA_DIR
):
"""
Init word2vec model
Args:
model_name_or_path: word2vec file path
w2v_kwargs: dict, params pass to the ``load_word2vec_format()`` function of ``gensim.models.KeyedVectors`` -
https://radimrehurek.com/gensim/models/keyedvectors.html#module-gensim.models.keyedvectors
stopwords: list, stopwords
cache_folder: str, save model dir
"""
from gensim.models import KeyedVectors # noqa
self.w2v_kwargs = w2v_kwargs if w2v_kwargs is not None else {}
if model_name_or_path and os.path.exists(model_name_or_path):
logger.info('Load pretrained model:{}'.format(model_name_or_path))
else:
model_dict = self.model_key_map.get(model_name_or_path, self.model_key_map['w2v-light-tencent-chinese'])
tar_filename = model_dict.get('tar_filename')
self.w2v_kwargs.update({'binary': model_dict.get('binary')})
url = model_dict.get('url')
untar_filename = model_dict.get('untar_filename')
# Set new model_name_or_path
old_model_name = model_name_or_path
model_name_or_path = os.path.join(cache_folder, untar_filename)
if old_model_name in self.model_key_map:
logger.info('Load pretrained model:{}, path:{}'.format(old_model_name, model_name_or_path))
else:
logger.warning(f"{old_model_name} not found, Set default model path: {model_name_or_path}")
if not os.path.exists(model_name_or_path):
logger.debug(f"Downloading {url} to {model_name_or_path}")
os.makedirs(cache_folder, exist_ok=True)
get_file(tar_filename, url, extract=True,
cache_dir=cache_folder,
cache_subdir=cache_folder,
verbose=1)
t0 = time.time()
w2v = KeyedVectors.load_word2vec_format(model_name_or_path, **self.w2v_kwargs)
# w2v.init_sims(replace=True)
logger.debug('Load w2v from {}, spend {:.2f} sec'.format(model_name_or_path, time.time() - t0))
self.stopwords = stopwords if stopwords else load_stopwords(default_stopwords_file)
self.w2v = w2v
self.jieba_tokenizer = JiebaTokenizer()
self.model_name_or_path = model_name_or_path
def __str__(self):
return f"<Word2Vec, word count: {len(self.w2v.key_to_index)}, emb size: {self.w2v.vector_size}, " \
f"stopwords count: {len(self.stopwords)}>"
def encode(self, sentences: Union[List[str], str], show_progress_bar: bool = False, **kwargs) -> ndarray:
"""
Encode sentences to vectors
"""
if self.w2v is None:
raise ValueError('No model for embed sentence')
input_is_string = False
if isinstance(sentences, str) or not hasattr(sentences, '__len__'):
sentences = [sentences]
input_is_string = True
all_embeddings = []
for sentence in tqdm(sentences, desc='Word2Vec Embeddings', disable=not show_progress_bar):
emb = []
count = 0
for word in sentence:
# 过滤停用词
if word in self.stopwords:
continue
# 调用词向量
if word in self.w2v.key_to_index:
emb.append(self.w2v.get_vector(word, norm=True))
count += 1
else:
if len(word) == 1:
continue
# 再切分，eg 特价机票
ws = self.jieba_tokenizer.tokenize(word, cut_all=True)
for w in ws:
if w in self.w2v.key_to_index:
emb.append(self.w2v.get_vector(w, norm=True))
count += 1
tensor_x = np.array(emb).sum(axis=0) # 纵轴相加
if count > 0:
avg_tensor_x = np.divide(tensor_x, count)
else:
avg_tensor_x = np.zeros(self.w2v.vector_size, dtype=float)
all_embeddings.append(avg_tensor_x)
all_embeddings = np.array(all_embeddings, dtype=float)
if input_is_string:
all_embeddings = all_embeddings[0]
return all_embeddings