From 12978f2ca314af96aff5194dee377163ec088d1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=89=87=E5=88=BB=E5=B0=8F=E5=93=A5=E5=93=A5?= Date: Fri, 30 Dec 2022 17:25:00 +0800 Subject: [PATCH] =?UTF-8?q?=E8=BF=98=E5=8E=9F=E5=88=A0=E9=99=A4=E4=BB=A3?= =?UTF-8?q?=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/py3.x/tensorflow2.x/text_bert.py | 114 +++++++++++++-------------- 1 file changed, 57 insertions(+), 57 deletions(-) diff --git a/src/py3.x/tensorflow2.x/text_bert.py b/src/py3.x/tensorflow2.x/text_bert.py index bec2ebee..2d31bcb9 100644 --- a/src/py3.x/tensorflow2.x/text_bert.py +++ b/src/py3.x/tensorflow2.x/text_bert.py @@ -93,62 +93,62 @@ def seq_padding(X, padding=0): ## bert / Embedding/ + lstm + crt - #%% - # 加载数据 - class TextBert(): - def __init__(self): - self.path_config = Config.bert.path_config - self.path_checkpoint = Config.bert.path_checkpoint - - self.token_dict = {} - with codecs.open(Config.bert.dict_path, 'r', 'utf8') as reader: - for line in reader: - token = line.strip() - self.token_dict[token] = len(self.token_dict) - - - def prepare_data(self): - neg = pd.read_excel(Config.bert.path_neg, header=None) - pos = pd.read_excel(Config.bert.path_pos, header=None) - data = [] - for d in neg[0]: - data.append((d, 0)) - for d in pos[0]: - data.append((d, 1)) - # 按照9:1的比例划分训练集和验证集 - random_order = list(range(len(data))) - np.random.shuffle(random_order) - train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0] - valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0] - return train_data, valid_data - - def build_model(self, m_type="bert"): - if m_type == "bert": - bert_model = load_trained_model_from_checkpoint(self.path_config, self.path_checkpoint, seq_len=None) - for l in bert_model.layers: - l.trainable = True - x1_in = Input(shape=(None,)) - x2_in = Input(shape=(None,)) - x = bert_model([x1_in, x2_in]) - x = Lambda(lambda x: x[:, 0])(x) - p = Dense(1, activation='sigmoid')(x)#根据分类种类自行调节，也可以多加一些层数 - model = Model([x1_in, x2_in], p) - model.compile( - loss='binary_crossentropy', - optimizer=Adam(1e-5), # 用足够小的学习率 - metrics=['accuracy'] - ) - else: - # 否则用 Embedding - model = Sequential() - model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True)) # Random embedding - model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True))) - crf = CRF(len(chunk_tags), sparse_target=True) - model.add(crf) - model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy]) - - model.summary() - return model +#%% +# 加载数据 +class TextBert(): + def __init__(self): + self.path_config = Config.bert.path_config + self.path_checkpoint = Config.bert.path_checkpoint + + self.token_dict = {} + with codecs.open(Config.bert.dict_path, 'r', 'utf8') as reader: + for line in reader: + token = line.strip() + self.token_dict[token] = len(self.token_dict) + + + def prepare_data(self): + neg = pd.read_excel(Config.bert.path_neg, header=None) + pos = pd.read_excel(Config.bert.path_pos, header=None) + data = [] + for d in neg[0]: + data.append((d, 0)) + for d in pos[0]: + data.append((d, 1)) + # 按照9:1的比例划分训练集和验证集 + random_order = list(range(len(data))) + np.random.shuffle(random_order) + train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0] + valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0] + return train_data, valid_data + + def build_model(self, m_type="bert"): + if m_type == "bert": + bert_model = load_trained_model_from_checkpoint(self.path_config, self.path_checkpoint, seq_len=None) + for l in bert_model.layers: + l.trainable = True + x1_in = Input(shape=(None,)) + x2_in = Input(shape=(None,)) + x = bert_model([x1_in, x2_in]) + x = Lambda(lambda x: x[:, 0])(x) + p = Dense(1, activation='sigmoid')(x)#根据分类种类自行调节，也可以多加一些层数 + model = Model([x1_in, x2_in], p) + model.compile( + loss='binary_crossentropy', + optimizer=Adam(1e-5), # 用足够小的学习率 + metrics=['accuracy'] + ) + else: + # 否则用 Embedding + model = Sequential() + model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True)) # Random embedding + model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True))) + crf = CRF(len(chunk_tags), sparse_target=True) + model.add(crf) + model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy]) + + model.summary() + return model #%% @@ -348,4 +348,4 @@ def _generator(): print('Fill with: ', list(map(lambda x: token_dict_rev[x], predicts[0][1:3]))) # Fill with: ['数', '学'] -# %% +# %% \ No newline at end of file