def train(self, trainingfile):
"""Starts model building"""
logger.info(
f'Training started with : learningRate:{self.config.learningRate!s}, epoch:{self.config.epoch!s}, ngrams :{self.config.ngrams!s}'
)
model = FastText()
if self.supervised:
model.supervised(input=trainingfile,
output=self.filepath,
epoch=self.config.epochs,
lr=self.config.learningRate,
wordNgrams=self.config.ngrams,
verbose=2,
minCount=1)
elif self.config.method == "cbow":
model.cbow(input=trainingfile,
output='model',
epoch=self.config.epoch,
lr=self.config.learningRate)
else:
model.skipgram(input=trainingfile,
output='model',
epoch=self.config.epoch,
lr=self.config.learningRate)
def train_pyfasttext_model():
# Skipgram model
model_sg = FastText()
# equals to: `./fasttext skipgram -input ../data/880w_news_title_content_seg_sort_uniq_head_2.txt -output lxw_model_sg_pyfasttext`
model_sg.skipgram(
input="../data/880w_news_title_content_seg_sort_uniq_head_2.txt",
output="../data/lxw_model_sg_pyfasttext")
# 自动生成文件../data/lxw_model_sg_pyfasttext.bin 和 ../data/lxw_model_sg_pyfasttext.vec
print(model_sg.words) # list of words in dictionary
# CBOW model
model_cbow = FastText()
# equals to: `./fasttext cbow -input ../data/880w_news_title_content_seg_sort_uniq_head_2.txt -output lxw_model_cbow_pyfasttext`
model_cbow.cbow(
input="../data/880w_news_title_content_seg_sort_uniq_head_2.txt",
output="../data/lxw_model_cbow_pyfasttext")
# 自动生成文件../data/lxw_model_cbow_pyfasttext.bin 和 ../data/lxw_model_cbow_pyfasttext.vec
print(model_cbow.words) # list of words in dictionary
print(type(model_cbow.words)) # <class 'list'>
# for word in skip_gram_model.words:
# print(word, skip_gram_model[word])
print(skip_gram_model.nearest_neighbors('贷款', k=2))
# test data is stored inside a file, use this:
# skip_gram_model.predict_proba_file('./test.txt', k=2)
print("\n")
##################
# 使用cbow模型训练 #
##################
cbow_model = FastText()
cbow_model.cbow(input='./train.txt', output='cbow_model', epoch=100, lr=0.7)
print(cbow_model['贷款'])
# print(cbow_model.get_numpy_vector('贷款'))
# print(cbow_model.get_numpy_vector('贷款', normalized=True))
var1 = cbow_model.get_numpy_vector('人民币')
var2 = cbow_model.get_numpy_vector('贷款')
var3 = cbow_model.get_numpy_vector('外币')
cbow_model.words_for_vector(var1 + var2 - var3, k=1)
# for word in cbow_model.words:
# print(word, cbow_model[word])
print(cbow_model.nearest_neighbors('贷款', k=2))
# test data is stored inside a file, use this:
