def test_vector(self):
text = '语言模型'
tokens1, segs1 = self.sjl_tokenizer.encode(text)
tokens2, segs2 = self.my_tokenizer.transform(text)
self.assertEqual(tokens1, tokens2)
self.assertEqual(segs1, segs2)
tokens1, segs1 = to_array([tokens1], [segs1])
tokens2, segs2 = to_array([tokens2], [segs2])
from bert4keras.models import build_transformer_model
print(build_transformer_model.__module__)
model = build_transformer_model(config_path, checkpoint_path)
res1 = model.predict([tokens1, segs1])
del model
gc.collect()
from garnet.models.build import build_transformer_model
print(build_transformer_model.__module__)
model = build_transformer_model(config_path, checkpoint_path)
res2 = model.predict([tokens2, segs2])
del model
gc.collect()
shape = res1.shape
self.assertEqual(np.sum(res1), np.sum(res2))
for k in range(shape[0]):
for i in range(shape[1]):
for j in range(shape[2]):
self.assertAlmostEqual(res1[k, i, j], res2[k, i, j])
def get_similarity_bert(strx, stry, bm ,tokenizer):
token_ids, segment_ids = tokenizer.encode(strx)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
a = bm.predict([token_ids, segment_ids])
token_ids, segment_ids = tokenizer.encode(stry)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
b = bm.predict([token_ids, segment_ids])
return cos_sim(a[0][0], b[0][0])
def generate(self, context):
token_ids, segment_ids = tokenizer.encode(context)
context_len = len(token_ids)
segment_id = segment_ids[-1] + 1
sentence = ''
words = []
gen_tokens = []
for i in range(self.maxlen):
token_ids.append(tokenizer._token_dict['[MASK]'])
segment_ids.append(segment_id)
tokens, segments = to_array([token_ids], [segment_ids])
probas = biden_model.predict([tokens, segments])[0]
# token = probas[context_len + i].argmax()
ids = np.argsort(probas[context_len + i])[::-1]
for token in ids:
if token not in gen_tokens:
gen_tokens.append(token)
break
words.append(tokenizer.decode([token]))
token_ids[context_len + i] = token
if token in self.end_id:
sentence = ' '.join(words)
return sentence
sentence = ' '.join(words)
sentence += '.'
return sentence
def recognize(self, text):
tokens = tokenizer.tokenize(text)
# while len(tokens) > 512:
# tokens.pop(-2)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = model.predict([token_ids, segment_ids])[0]
labels = self.decode(nodes)
entities, starting = [], False
for i, label in enumerate(labels):
if label > 0:
if label % 2 == 1:
starting = True
entities.append([[i], id2label[(label - 1) // 2]])
else:
if starting:
entities[-1][0].append(i)
# else:
# starting = False
else:
starting = False
ner_answer = []
for w, l in entities:
ner_answer.append([mapping[w[0]][0], mapping[w[-1]][-1] + 1, l])
return ner_answer
def recognize(self, text, tokenizer, models, loader):
tokens = tokenizer.tokenize(text) # 将文本切分成字符
mapping = tokenizer.rematch(text, tokens) # 将字符按顺序映射成id
token_ids = tokenizer.tokens_to_ids(tokens) # 将字符按字典映射成id
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = 0
if isinstance(models, list):
for model in models:
nodes += model.predict([token_ids, segment_ids
])[0] # shape[len(text), 27]
nodes /= len(models)
else:
nodes = models.predict([token_ids,
segment_ids])[0] # shape[len(text), 27]
labels = self.decode(nodes)
entities, starting = [], False
for i, label in enumerate(labels):
if label > 0:
if label % 2 == 1: # 如果标签id为奇数则为实体起始位置
starting = True
entities.append([[i], loader.id2label[(label - 1) // 2]
]) # 根据起始位置的label确定实体类别
elif starting:
entities[-1][0].append(
i) # 实体内部字符的下标加入列表，第二个维度只有一个值为类别名称，所以都取0
else:
starting = False
else:
starting = False
return [(text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1], l)
for w, l in entities]
def recognize(self, text):
"""
:param text:
:return:
"""
tokens = tokenizer.tokenize(text)
while len(tokens) > 512:
tokens.pop(-2)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = model.predict([token_ids, segment_ids])[0]
labels = self.decode(nodes)
entities, starting = [], False
for i, label in enumerate(labels):
if label > 0:
if label % 2 == 1:
starting = True
entities.append([[i], id2label[(label - 1) // 2]])
elif starting:
entities[-1][0].append(i)
else:
starting = False
else:
starting = False
return [(text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1], l)
for w, l in entities]
def extract_spoes(text, threshold=0):
"""抽取输入text所包含的三元组
"""
tokens = tokenizer.tokenize(text, maxlen=maxlen)
mapping = tokenizer.rematch(text, tokens)
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
outputs = model.predict([token_ids, segment_ids])
outputs = [o[0] for o in outputs]
# 抽取subject和object
subjects, objects = set(), set()
outputs[0][:, [0, -1]] -= np.inf
outputs[0][:, :, [0, -1]] -= np.inf
for l, h, t in zip(*np.where(outputs[0] > threshold)):
if l == 0:
subjects.add((h, t))
else:
objects.add((h, t))
# 识别对应的predicate
spoes = set()
for sh, st in subjects:
for oh, ot in objects:
p1s = np.where(outputs[1][:, sh, oh] > threshold)[0]
p2s = np.where(outputs[2][:, st, ot] > threshold)[0]
ps = set(p1s) & set(p2s)
for p in ps:
spoes.add(
(text[mapping[sh][0]:mapping[st][-1] + 1], id2predicate[p],
text[mapping[oh][0]:mapping[ot][-1] + 1]))
return list(spoes)
def recognize(self, text):
########### 为了获取输入向量
tokens = tokenizer.tokenize(text)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
############
############ segment_ids往往是用来判断输入几句话的 同一输入的是一句话 因此全部是0
segment_ids = [0] * len(token_ids)
############
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = model.predict([token_ids, segment_ids])[0]
labels = self.decode(nodes)
entities, starting = [], False
for i, label in enumerate(labels):
if label > 0:
if label % 2 == 1:
starting = True
entities.append([[i], id2label[(label - 1) // 2]])
elif starting:
entities[-1][0].append(i)
else:
starting = False
else:
starting = False
return [(text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1], l)
for w, l in entities]
def extract_features(self, text: str):
"""
编码测试
:return:
"""
token_ids, segment_ids = self.tokenizer.encode(u'{}'.format(text))
token_ids, segment_ids = to_array([token_ids], [segment_ids])
print("\n === features === \n")
print(self.predict([token_ids, segment_ids]))
def vec2(tex):
# 编码测试
token_ids, segment_ids = tokenizer.encode(tex)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
tmp = model.predict([token_ids, segment_ids])[:, 0, :]
# print(tmp)
return tmp
def vec2(tex):
# 编码测试
token_ids, segment_ids = tokenizer.encode(tex)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
global tmp
# tmp=model.predict([token_ids, segment_ids])[:,0,:] # 0代表取[CLS]
tmp3=tmp(tex)
# print(tmp)
return tmp3
def recognize(self, text, threshold=0):
tokens = tokenizer.tokenize(text, maxlen=512)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
scores = model.predict([token_ids, segment_ids])[0]
scores[:, [0, -1]] -= np.inf
scores[:, :, [0, -1]] -= np.inf
entities = []
for l, start, end in zip(*np.where(scores > threshold)):
entities.append(
(mapping[start][0], mapping[end][-1], categories[l]))
return entities
def evaluate(data, Seq_ner, Tag_ner, model):
"""评测函数
"""
token_list, seq_list, tag_list = data
X, Y, Z = 1e-10, 1e-10, 1e-10
for token, seq, tag in tqdm(zip(token_list, seq_list, tag_list)):
token_ids = to_array([token])
P = model.predict([token_ids])
S, T = list(Seq_ner.decode(P[0][0])), list(Tag_ner.decode(P[1][0]))
X += 1 if (S == seq) else 0
Y += 1 if (T == tag) else 0
Z += 1
seq_acc, tag_acc = X / Z, Y / Z
return seq_acc, tag_acc
def test_masked_predict(self):
text = "科学技术是第一生产力"
tokens, segs = self.tokenizer.transform(text)
print(tokens)
tokens[3] = tokens[4] = self.tokenizer.token2id(
self.tokenizer.token_mask)
print(tokens)
tokens, segs = to_array([tokens], [segs])
probs = self.model.predict([tokens, segs])[1][0]
pred_ids = probs.argmax(axis=1)
print(pred_ids)
text = self.tokenizer.reverse_transform(list(pred_ids))
print(text)
self.assertEqual(text[3:5], '技术')
def predict_to_file(in_file, out_file):
"""输出预测结果到文件
结果文件可以提交到 https://www.cluebenchmarks.com 评测。
"""
fw = open(out_file, 'w')
with open(in_file) as fr:
for l in tqdm(fr):
l = json.loads(l)
text = l['sentence']
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
label = model.predict([token_ids, segment_ids])[0].argmax()
l = json.dumps({'id': str(l['id']), 'label': str(label)})
fw.write(l + '\n')
fw.close()
def __iter__(self, random=False):
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
for is_end, (text, label) in self.sample(random):
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
token_ids, segment_ids = to_array([token_ids, segment_ids])
batch_token_ids.append(token_ids)
batch_segment_ids.append(segment_ids)
batch_labels.append([label])
if len(batch_token_ids) == self.batch_size or is_end:
batch_token_ids = sequence_padding(batch_token_ids)
batch_segment_ids = sequence_padding(batch_segment_ids)
batch_labels = sequence_padding(batch_labels)
yield [batch_token_ids, batch_segment_ids], batch_labels
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
def tokenize(self, text):
tokens = tokenizer.tokenize(text)
while len(tokens) > 512:
tokens.pop(-2)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = model.predict([token_ids, segment_ids])[0]
labels = self.decode(nodes)
words = []
for i, label in enumerate(labels[1:-1]):
if label < 2 or len(words) == 0:
words.append([i + 1])
else:
words[-1].append(i + 1)
return [text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1] for w in words]
def extract_spoes(text):
"""抽取输入text所包含的三元组
"""
tokens = tokenizer.tokenize(text, maxlen=maxlen)
mapping = tokenizer.rematch(text, tokens)
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
# 抽取subject
subject_preds = subject_model.predict([token_ids, segment_ids])
subject_preds[:, [0, -1]] *= 0
start = np.where(subject_preds[0, :, 0] > 0.6)[0]
end = np.where(subject_preds[0, :, 1] > 0.5)[0]
subjects = []
for i in start:
j = end[end >= i]
if len(j) > 0:
j = j[0]
subjects.append((i, j))
if subjects:
spoes = []
token_ids = np.repeat(token_ids, len(subjects), 0)
segment_ids = np.repeat(segment_ids, len(subjects), 0)
subjects = np.array(subjects)
# 传入subject，抽取object和predicate
object_preds = object_model.predict([token_ids, segment_ids, subjects])
object_preds[:, [0, -1]] *= 0
for subject, object_pred in zip(subjects, object_preds):
start = np.where(object_pred[:, :, 0] > 0.6)
end = np.where(object_pred[:, :, 1] > 0.5)
for _start, predicate1 in zip(*start):
for _end, predicate2 in zip(*end):
if _start <= _end and predicate1 == predicate2:
spoes.append(
((mapping[subject[0]][0],
mapping[subject[1]][-1]), predicate1,
(mapping[_start][0], mapping[_end][-1]))
)
break
return [(text[s[0]:s[1] + 1], id2predicate[p], text[o[0]:o[1] + 1])
for s, p, o, in spoes]
else:
return []
def recognize(self, text):
tokens = tokenizer.tokenize(text, maxlen=512)
mapping = tokenizer.rematch(text, tokens)
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
nodes = model.predict([token_ids, segment_ids])[0]
labels = self.decode(nodes)
entities, starting = [], False
for i, label in enumerate(labels):
if label > 0:
if label % 2 == 1:
starting = True
entities.append([[i], categories[(label - 1) // 2]])
elif starting:
entities[-1][0].append(i)
else:
starting = False
else:
starting = False
return [(mapping[w[0]][0], mapping[w[-1]][-1], l) for w, l in entities]
def test_load_and_save(self):
current_folder = os.path.abspath(
os.path.dirname(os.path.realpath(__file__)))
bert_path = os.path.join(current_folder, 'assets', 'bert_sample_model')
config_path = os.path.join(bert_path, 'bert_config.json')
checkpoint_path = os.path.join(bert_path, 'bert_model.ckpt')
dict_path = os.path.join(bert_path, 'vocab.txt')
bert_model = build_transformer_model(config_path=config_path,
checkpoint_path=checkpoint_path,
model='bert',
application='encoder',
return_keras_model=True)
tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分词器
# 编码测试
token_ids, segment_ids = tokenizer.encode(u'jack play all day')
token_ids, segment_ids = to_array([token_ids], [segment_ids])
print('\n ===== predicting =====\n')
print(bert_model.predict([token_ids, segment_ids]))
# Serialize model
_ = bert_model.to_json()
) # 建立模型，加载权重
sentences = []
init_sent = u'科学技术是第一生产力。' # 给定句子或者None
minlen, maxlen = 8, 32
steps = 10000
converged_steps = 1000
vocab_size = tokenizer._vocab_size
if init_sent is None:
length = np.random.randint(minlen, maxlen + 1)
tokens = ['[CLS]'] + ['[MASK]'] * length + ['[SEP]']
token_ids = tokenizer.tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
else:
token_ids, segment_ids = tokenizer.encode(init_sent)
length = len(token_ids) - 2
for _ in tqdm(range(steps), desc='Sampling'):
# Gibbs采样流程：随机mask掉一个token，然后通过MLM模型重新采样这个token。
i = np.random.choice(length) + 1
token_ids[i] = tokenizer._token_mask_id
probas = model.predict(to_array([token_ids], [segment_ids]))[0, i]
token = np.random.choice(vocab_size, p=probas)
token_ids[i] = token
sentences.append(tokenizer.decode(token_ids))
print(u'部分随机采样结果：')
for _ in range(10):
print(np.random.choice(sentences[converged_steps:]))
def bert_feature_extract(txt):
# 编码测试
token_ids, segment_ids = tokenizer.encode(txt)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
# print()
return model.predict([token_ids, segment_ids])[0][0]
def toids(s):
token_ids, segment_ids = tokenizer.encode(s)
token_ids, segment_ids = to_array([token_ids], [segment_ids])
return [token_ids, segment_ids]
import numpy as np
from bert4keras.backend import keras
from bert4keras.models import build_transformer_model
from bert4keras.tokenizers import Tokenizer
from bert4keras.snippets import to_array
config_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/root/kg/bert/chinese_L-12_H-768_A-12/vocab.txt'
tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分词器
model = build_transformer_model(config_path, checkpoint_path) # 建立模型，加载权重
# 编码测试
token_ids, segment_ids = tokenizer.encode(u'语言模型')
token_ids, segment_ids = to_array([token_ids], [segment_ids])
print('\n ===== predicting =====\n')
print(model.predict([token_ids, segment_ids]))
"""
输出：
[[[-0.63251007 0.2030236 0.07936534 ... 0.49122632 -0.20493352
0.2575253 ]
[-0.7588351 0.09651865 1.0718756 ... -0.6109694 0.04312154
0.03881441]
[ 0.5477043 -0.792117 0.44435206 ... 0.42449304 0.41105673
0.08222899]
[-0.2924238 0.6052722 0.49968526 ... 0.8604137 -0.6533166
0.5369075 ]
[-0.7473459 0.49431565 0.7185162 ... 0.3848612 -0.74090636
0.39056838]
def sentiment(text):
token_ids, segment_ids = tokenizer.encode(text,
maxlen=maxlen) # word encoding
token_ids, segment_ids = to_array([token_ids], [segment_ids])
y_pred = model.predict([token_ids, segment_ids]).argmax(axis=1)
return y_pred
