def train(self):
x_items, train_y, valid_x, valid_y = self.read_message('car/train.csv')
# 获取bert字向量
model = BLSTMModel(bert)
# 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(list(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')
file.write("id,flag\n")
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):
# filepath = "saved-model-{epoch:02d}-{acc:.2f}.hdf5"
# checkpoint_callback = ModelCheckpoint(filepath,
# monitor='acc',
# verbose=1)
x_items, train_y = self.read_message('../data/yingyangshi/train.txt')
x_dev, dev_y = self.read_message('../data/yingyangshi/dev.txt')
# 获取bert字向量
bert = BERTEmbedding('textclassfation/input0/chinese_L-12_H-768_A-12')
model = BLSTMModel(bert)
# model.build_multi_gpu_model(gpus=2)
model.fit(x_items, train_y, x_dev, dev_y, epochs=2, batch_size=64)
# 保存模型
model.save("../健康管理师单选分字BERT-model")
def test_bert_model(self):
embedding = BERTEmbedding(bert_path,
task=kashgari.CLASSIFICATION,
sequence_length=100)
model = BLSTMModel(embedding=embedding)
model.fit(valid_x, valid_y, epochs=1)
res = model.predict(valid_x[:20])
assert True
model_path = os.path.join(tempfile.gettempdir(), str(time.time()))
model.save(model_path)
new_model = kashgari.utils.load_model(model_path)
new_res = new_model.predict(valid_x[:20])
assert np.array_equal(new_res, res)
def train(self):
x_items, train_y = self.read_message('../data/Chinese medicine licensed pharmacist/train.txt')
x_dev, dev_y = self.read_message('../data/Chinese medicine licensed pharmacist/dev.txt')
# 获取bert字向量
model = BLSTMModel()
# 输入模型训练数据 标签 步数
model.fit(x_items,
train_y,
x_dev,
dev_y,
batch_size=32,
epochs=20,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
model.save("../model/中医执业药师char-model")
def train(self):
x_xiyao, xiyao_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/dev.txt')
# 获取bert字向量
bert = BERTEmbedding('bert-base-chinese', sequence_length=200)
model = BLSTMModel(bert)
# 输入模型训练数据 标签 步数
model.fit(x_xiyao,
xiyao_y,
x_dev,
dev_y,
epochs=8,
batch_size=256,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
model.save("../西药执业药师-model")
def train(self):
x_items, train_y = self.read_message('../data/健康管理师分类数据集/train.txt')
x_xiyao, xiyao_y = self.read_message('../data/西药执业药师/train.txt')
x_yingyangshi, yingyangshi_y = self.read_message(
'../data/yingyangshi/train.txt')
x_items.extend(x_xiyao)
train_y.extend(xiyao_y)
x_items.extend(x_yingyangshi)
train_y.extend(yingyangshi_y)
x_dev, dev_y = self.read_message('../data/健康管理师分类数据集/valid.txt')
# 获取bert字向量
bert = BERTEmbedding('bert-base-chinese', sequence_length=200)
model = BLSTMModel(bert)
# 输入模型训练数据 标签 步数
model.fit(x_items,
train_y,
x_dev,
dev_y,
epochs=8,
batch_size=128,
fit_kwargs={'callbacks': [tf_board_callback]})
# 保存模型
model.save("../健康管理师分字BERT-model")
log_filepath = r"D:\data\biendata\ccks2019_el\clf_log"
early_stop = EarlyStopping(monitor="val_loss", mode="min", patience=2)
# early_stop = EarlyStopping(monitor="val_acc", mode="max", patience=2)
log = TensorBoard(log_dir=log_filepath,
write_images=False,
write_graph=True,
histogram_freq=0)
emn_path = r'D:\data\bert\chinese_L-12_H-768_A-12'
embedding = BERTEmbedding(emn_path, sequence_length=1024)
emn_path = r'D:/data/word2vec/zh/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5.utf8.txt'
embedding = WordEmbeddings(emn_path, sequence_length=1024)
# model = DropoutBGRUModel(embedding)
model = BLSTMModel(embedding)
model.fit(train_x[:100000],
train_y[:100000],
x_validate=validate_x[:20000],
y_validate=validate_y[:20000],
epochs=20,
batch_size=256,
labels_weight=True,
fit_kwargs={'callbacks': [early_stop, log]})
model.evaluate(test_x, test_y)
model.save(model_path)
class BLSTMModelModelTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(BLSTMModelModelTest, self).__init__(*args, **kwargs)
self.__model_class__ = BLSTMModel
self.x_data = [
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'),
]
self.y_data = ['a', 'a', 'a', 'b', 'c']
self.x_eval = [
list('语言学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('在语言结构研究与意义研究之间存在一个重要的主题划分。'),
list('语法中包含了词法，句法以及语音。'),
list('语音学是语言学的一个相关分支，它涉及到语音与非语音声音的实际属性，以及它们是如何发出与被接收到的。'),
list('与学习语言不同，语言学是研究所有人类语文发展有关的一门学术科目。'),
]
self.y_eval = ['a', 'a', 'a', 'b', 'c', 'a']
def prepare_model(self, embedding: BaseEmbedding = None):
self.model = self.__model_class__(embedding)
def test_build(self):
self.prepare_model()
self.model.fit(self.x_data, self.y_data)
self.assertEqual(len(self.model.label2idx), 4)
self.assertGreater(len(self.model.token2idx), 4)
logging.info(self.model.embedding.token2idx)
def test_fit(self):
self.prepare_model()
self.model.fit(self.x_data,
self.y_data,
x_validate=self.x_eval,
y_validate=self.y_eval)
def test_label_token_convert(self):
self.test_fit()
self.assertTrue(isinstance(self.model.convert_label_to_idx('a'), int))
self.assertTrue(isinstance(self.model.convert_idx_to_label(1), str))
self.assertTrue(
all(
isinstance(i, int)
for i in self.model.convert_label_to_idx(['a'])))
self.assertTrue(
all(
isinstance(i, str)
for i in self.model.convert_idx_to_label([1, 2])))
sentence = 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')
tokens = self.model.embedding.tokenize(sentence)
self.assertEqual(len(sentence) + 2, len(tokens))
def test_predict(self):
self.test_fit()
sentence = list('语言学包含了几种分支领域。')
self.assertTrue(isinstance(self.model.predict(sentence), str))
self.assertTrue(isinstance(self.model.predict([sentence]), list))
logging.info('test predict: {} -> {}'.format(
sentence, self.model.predict(sentence)))
def test_eval(self):
self.test_fit()
self.model.evaluate(self.x_data, self.y_data)
def test_bert(self):
embedding = BERTEmbedding('chinese_L-12_H-768_A-12',
sequence_length=30)
self.prepare_model(embedding)
self.model.fit(self.x_data,
self.y_data,
x_validate=self.x_eval,
y_validate=self.y_eval)
sentence = list('语言学包含了几种分支领域。')
logging.info(self.model.embedding.tokenize(sentence))
logging.info(self.model.predict(sentence))
self.assertTrue(isinstance(self.model.predict(sentence), str))
self.assertTrue(isinstance(self.model.predict([sentence]), list))
def test_word2vec_embedding(self):
embedding = WordEmbeddings('sgns.weibo.bigram',
sequence_length=30,
limit=5000)
self.prepare_model(embedding)
self.model = BLSTMModel(embedding=embedding)
self.model.fit(self.x_data,
self.y_data,
x_validate=self.x_eval,
y_validate=self.y_eval)
sentence = list('语言学包含了几种分支领域。')
logging.info(self.model.embedding.tokenize(sentence))
logging.info(self.model.predict(sentence))
self.assertTrue(isinstance(self.model.predict(sentence), str))
self.assertTrue(isinstance(self.model.predict([sentence]), list))
def test_save_and_load(self):
self.test_fit()
model_path = tempfile.gettempdir()
self.model.save(model_path)
new_model = BLSTMModel.load_model(model_path)
self.assertIsNotNone(new_model)
sentence = list('语言学包含了几种分支领域。')
result = new_model.predict(sentence)
self.assertTrue(isinstance(result, str))
