def train(self):
x_items, train_y, valid_x, valid_y = self.read_message('car/train.csv')
# 获取bert字向量
model = CNNModel()
# 输入模型训练数据 标签 步数
model.fit(x_items,
train_y,
valid_x,
valid_y,
batch_size=64,
epochs=12,
callbacks=[tf_board_callback])
# 保存模型
file = pd.read_csv("car/test.csv", encoding='utf-8').values.tolist()
test_data = []
id_list = []
for i in file:
test_data.append(jieba.lcut(str(i[1]) + str(i[2])))
id_list.append(i[0])
predict_answers = model.predict(x_data=test_data)
file = open("data/test_predict_bert_car.csv", 'w', encoding='utf-8')
for i, j in zip(id_list, predict_answers):
i = i.strip()
file.write(str(i) + "," + str(j) + "\n")
model.save("../model/news-classification-bert-model")
def train(self):
x_items, train_y = read_message()
# 获取bert字向量
bert = BERTEmbedding(self.bert_place, sequence_length=256)
model = CNNModel(bert)
# 输入模型训练数据 标签 步数
model.fit(x_items,
train_y,
epochs=200,
batch_size=32,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
model.save("output/classification-model")
model.evaluate(x_items, train_y)
def train():
x_items, train_y = read_message()
# 获取bert字向量
model = CNNModel(bert)
# 输入模型训练数据 标签 步数
model.fit(x_items,
train_y,
epochs=20,
class_weight=True,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
model.save("../classification-model")
for i in x_items:
result = model.predict(i)
print("\n" + result)
def train(self):
x_train, train_y = self.read_message('../data/西药执业药师/train.txt')
x_dev, dev_y = self.read_message('../data/西药执业药师/test.txt')
x_test, test_y = self.read_message('../data/西药执业药师/dev.txt')
# 获取bert字向量
bert = BERTEmbedding('bert-base-chinese', sequence_length=100)
# 获取词向量
# embedding = WordEmbeddings('sgns.weibo.bigram.bz2', 50)
long_model = CNNModel(bert)
# 输入模型训练数据 标签 步数
long_model.fit(x_train,
train_y,
x_dev,
dev_y,
epochs=20,
batch_size=128,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
long_model.save("../classification-model")
result = long_model.evaluate(x_test, test_y)
return result
import tqdm
import jieba
from kashgari.tasks.classification import CNNModel
def read_data_file(path):
lines = open(path, 'r', encoding='utf-8').read().splitlines()
x_list = []
y_list = []
for line in tqdm.tqdm(lines):
rows = line.split('\t')
if len(rows) >= 2:
y_list.append(rows[0])
x_list.append(list(jieba.cut('\t'.join(rows[1:]))))
else:
print(rows)
return x_list, y_list
test_x, test_y = read_data_file('cnews/cnews.test.txt')
train_x, train_y = read_data_file('cnews/cnews.train.txt')
val_x, val_y = read_data_file('cnews/cnews.val.txt')
model = CNNModel()
model.fit(train_x, train_y, val_x, val_y, batch_size=128)
result = model.evaluate(test_x, test_y)
model.save('model/kashgari/cnn')
word2idx[k.BOS] = word2idx['pad']
word2idx[k.EOS] = word2idx['pad']
self.token2idx = word2idx
def build_token2idx_dict(self,
x_data: List[TextSeqType],
min_count: int = 5):
logging.debug(
"word2vec embedding no need to build token2idx with corpus")
if __name__ == '__main__':
train_x = [
list('语言学（英语：linguistics）是一门关于人类语言的科学研究'),
list('语言学（英语：linguistics）是一门关于人类语言的科学研究'),
list('语言学（英语：linguistics）是一门关于人类语言的科学研究'),
list('语言学包含了几种分支领域。'),
list('在语言结构（语法）研究与意义（语义与语用）研究之间存在一个重要的主题划分'),
]
train_y = ['a', 'a', 'a', 'b', 'c']
from kashgari.utils.logger import init_logger
from kashgari.tasks.classification import CNNModel
init_logger()
embedding = GPT2Embedding(
'/Users/brikerman/Desktop/python/gpt-2/models/117M', 10)
r = embedding.embed(['hello', 'world'])
model = CNNModel(embedding)
model.fit(train_x, train_y, epochs=20)
print(r.shape)
tf_board_callback = keras.callbacks.TensorBoard(log_dir='tf_dir', update_freq=10)
from kashgari.tasks.classification import CNNLSTMModel, CNNModel
save = ModelCheckpoint(
os.path.join('model_dir', 'CNNModel_bert.h5'),
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='auto'
)
early_stopping = EarlyStopping(
monitor='val_acc',
min_delta=0,
patience=8,
verbose=1,
mode='auto'
)
model = CNNModel(embed)
# ------------ build model ------------
model.fit(
train_features, train_labels,
valid_features, valid_labels,
epochs=60,
batch_size=256,
callbacks=[tf_board_callback, save, early_stopping]
)
model.evaluate(test_features, test_labels)
