def collate(examples):
p_ids, examples = zip(*examples)
p_ids = torch.tensor([p_id for p_id in p_ids], dtype=torch.long)
batch_token_ids, batch_segment_ids = [], []
batch_token_type_ids, batch_subject_labels, batch_subject_ids, batch_object_labels = [], [], [], []
for example in examples:
# todo maxlen
token_ids, segment_ids = self.tokenizer.encode(
example.context, max_length=self.max_len)
example.bert_tokens = self.tokenizer.tokenize(example.context)
example.token_ids = token_ids
if self.is_train:
spoes = {}
for s, p, o in example.gold_answer:
s = self.tokenizer.encode(s)[0][1:-1]
p = BAIDU_RELATION[p]
o = self.tokenizer.encode(o)[0][1:-1]
s_idx = search(s, token_ids)
o_idx = search(o, token_ids)
if s_idx != -1 and o_idx != -1:
s = (s_idx, s_idx + len(s) - 1)
o = (o_idx, o_idx + len(o) - 1, p)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
if spoes:
# subject标签
token_type_ids = np.zeros(len(token_ids),
dtype=np.long)
subject_labels = np.zeros((len(token_ids), 2),
dtype=np.float32)
for s in spoes:
subject_labels[s[0], 0] = 1
subject_labels[s[1], 1] = 1
# 随机选一个subject
start, end = np.array(list(spoes.keys())).T
start = np.random.choice(start)
end = np.random.choice(end[end >= start])
token_type_ids[start:end + 1] = 1
subject_ids = (start, end)
# 对应的object标签
object_labels = np.zeros(
(len(token_ids), len(BAIDU_RELATION), 2),
dtype=np.float32)
for o in spoes.get(subject_ids, []):
object_labels[o[0], o[2], 0] = 1
object_labels[o[1], o[2], 1] = 1
batch_token_ids.append(token_ids)
batch_token_type_ids.append(token_type_ids)
batch_segment_ids.append(segment_ids)
batch_subject_labels.append(subject_labels)
batch_subject_ids.append(subject_ids)
batch_object_labels.append(object_labels)
else:
batch_token_ids.append(token_ids)
batch_segment_ids.append(segment_ids)
batch_token_ids = sequence_padding(batch_token_ids, is_float=False)
batch_segment_ids = sequence_padding(batch_segment_ids,
is_float=False)
if not self.is_train:
return p_ids, batch_token_ids, batch_segment_ids
else:
batch_token_type_ids = sequence_padding(batch_token_type_ids,
is_float=False)
batch_subject_ids = torch.tensor(batch_subject_ids)
batch_subject_labels = sequence_padding(batch_subject_labels,
padding=np.zeros(2),
is_float=True)
batch_object_labels = sequence_padding(
batch_object_labels,
padding=np.zeros((len(BAIDU_RELATION), 2)),
is_float=True)
return batch_token_ids, batch_segment_ids, batch_token_type_ids, batch_subject_ids, batch_subject_labels, batch_object_labels
def _read(self, filename, data_type):
examples = []
with codecs.open(filename, 'r') as f:
gold_num = 0
p_id = 0
for line in tqdm(f):
p_id += 1
data_json = json.loads(line.strip())
text_raw = data_json['text'].lower()
tokens = []
for token in text_raw:
tokens.append(token)
if len(tokens) >= self.max_seq_length - 2:
break
tokens = ["[CLS]"] + tokens + ["[SEP]"]
sub_po_dict, sub_ent_list, spo_list = dict(), list(), list()
spoes = {}
for spo in data_json['spo_list']:
for spo_object in spo['object'].keys():
# assign relation label
if spo['predicate'] in self.spo_conf:
# simple relation
predicate_label = self.spo_conf[spo['predicate']]
subject_sub_tokens = list(spo['subject'])
object_sub_tokens = list(spo['object']['@value'])
# todo 补充spo_v2版本时的处理逻辑
sub_ent_list.append(spo['subject'].lower())
spo_list.append(
(spo['subject'].lower(), spo['predicate'],
spo['object']['@value'].lower()))
else:
# complex relation
predicate_label = self.spo_conf[spo['predicate'] +
'_' + spo_object]
subject_sub_tokens = list(spo['subject'])
object_sub_tokens = list(spo['object'][spo_object])
subject_start, object_start = search_spo_index(
tokens, subject_sub_tokens, object_sub_tokens)
if subject_start == -1:
subject_start = search(subject_sub_tokens, tokens)
if object_start == -1:
object_start = search(object_sub_tokens, tokens)
if subject_start != -1 and object_start != -1:
s = (subject_start,
subject_start + len(subject_sub_tokens) - 1)
o = (object_start,
object_start + len(object_sub_tokens) - 1,
predicate_label)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
examples.append(
Example(p_id=p_id,
context=text_raw,
bert_tokens=tokens,
sub_entity_list=list(set(sub_ent_list)),
gold_answer=spo_list,
spoes=spoes))
gold_num += len(set(spo_list))
print('total gold num is {}'.format(gold_num))
logging.info("{} total size is {} ".format(data_type, len(examples)))
return examples
def collate(examples):
p_ids, examples = zip(*examples)
p_ids = torch.tensor([p_id for p_id in p_ids], dtype=torch.long)
batch_char_ids, batch_word_ids = [], []
batch_token_type_ids, batch_subject_labels, batch_subject_ids, batch_object_labels = [], [], [], []
for example in examples:
# todo maxlen
char_ids = [
self.char2idx.get(char, 1) for char in example.context
]
word_ids = [
self.word2idx.get(word, 0) for word in example.text_word
for _ in word
]
if len(char_ids) != len(word_ids):
print(example.context)
print(char_ids)
print(len(char_ids))
print(example.text_word)
print(word_ids)
print(len(word_ids))
assert len(char_ids) == len(word_ids)
char_ids = char_ids[:self.max_len]
word_ids = word_ids[:self.max_len]
# example.context = example.context[:self.max_len]
if self.is_train:
spoes = {}
for s, p, o in example.gold_answer:
s = [self.char2idx.get(s_, 1) for s_ in s]
p = BAIDU_RELATION[p]
o = [self.char2idx.get(o_, 1) for o_ in o]
s_idx = search(s, char_ids)
o_idx = search(o, char_ids)
if s_idx != -1 and o_idx != -1:
s = (s_idx, s_idx + len(s) - 1)
o = (o_idx, o_idx + len(o) - 1, p)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
if spoes:
# subject标签
token_type_ids = np.zeros(len(char_ids), dtype=np.long)
subject_labels = np.zeros((len(char_ids), 2),
dtype=np.float32)
for s in spoes:
subject_labels[s[0], 0] = 1
subject_labels[s[1], 1] = 1
# 随机选一个subject
start, end = np.array(list(spoes.keys())).T
start = np.random.choice(start)
end = np.random.choice(end[end >= start])
token_type_ids[start:end + 1] = 1
subject_ids = (start, end)
# 对应的object标签
object_labels = np.zeros(
(len(char_ids), len(BAIDU_RELATION), 2),
dtype=np.float32)
for o in spoes.get(subject_ids, []):
object_labels[o[0], o[2], 0] = 1
object_labels[o[1], o[2], 1] = 1
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_token_type_ids.append(token_type_ids)
batch_subject_labels.append(subject_labels)
batch_subject_ids.append(subject_ids)
batch_object_labels.append(object_labels)
else:
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_char_ids = sequence_padding(batch_char_ids, is_float=False)
batch_word_ids = sequence_padding(batch_word_ids, is_float=False)
if not self.is_train:
return p_ids, batch_char_ids, batch_word_ids
else:
batch_token_type_ids = sequence_padding(batch_token_type_ids,
is_float=False)
batch_subject_ids = torch.tensor(batch_subject_ids)
batch_subject_labels = sequence_padding(batch_subject_labels,
padding=np.zeros(2),
is_float=True)
batch_object_labels = sequence_padding(
batch_object_labels,
padding=np.zeros((len(BAIDU_RELATION), 2)),
is_float=True)
return batch_char_ids, batch_word_ids, batch_token_type_ids, batch_subject_ids, batch_subject_labels, batch_object_labels
def _read(self, filename, data_type):
'''
构建训练数据集
complex relation
当面对complex的sample 比如 下面这组关系
[{subject:'张智尧','predicate':'饰演','object':{'@value':'楚留香','inWork':'楚留香传奇'}},
{subject:'楚留香传奇'，‘predicate’:'主演'，'object':{'@value':'张智尧'}}]
我们需要需要把它改写成:
{('楚留香传奇'):(‘张智尧‘，’主演‘),(楚留香):('楚留香传奇','饰演_inwork'),
('张智尧'):('楚留香'，‘饰演_@value’)}
插入example
'''
examples = []
with open(filename, 'r') as fr:
p_id = 0
for line in tqdm(fr.readlines()):
p_id += 1
src_data = json.loads(line)
text_raw = src_data['text']
text_raw = text_raw.replace('®', '')
text_raw = text_raw.replace('◆', '')
tokens, tok_to_orig_start_index, tok_to_orig_end_index = covert_to_tokens(
text_raw,
self.tokenizer,
self.max_seq_length,
return_orig_index=True)
tokens = ["[CLS]"] + tokens + ["[SEP]"]
sub_po_dict, sub_ent_list, spo_list = dict(), list(), list()
if 'spo_list' not in src_data:
examples.append(
Example(
p_id=p_id,
raw_text=src_data['text'],
context=text_raw,
tok_to_orig_start_index=tok_to_orig_start_index,
tok_to_orig_end_index=tok_to_orig_end_index,
bert_tokens=tokens,
sub_entity_list=None,
gold_answer=None,
spoes=None))
else:
spoes = {}
for spo in src_data['spo_list']:
spo_dict = dict()
for spo_object in spo['object'].keys():
if spo['predicate'] in self.spo_conf:
label = spo['predicate']
else:
label = spo['predicate'] + '_' + spo_object
spo_dict[self.spo_conf[label]] = spo['object'][
spo_object]
for spo_object in spo['object'].keys():
# assign relation label
if spo['predicate'] in self.spo_conf:
# simple relation
predicate_label = self.spo_conf[
spo['predicate']]
subject_sub_tokens = covert_to_tokens(
spo['subject'], self.tokenizer,
self.max_seq_length)
object_sub_tokens = covert_to_tokens(
spo['object']['@value'], self.tokenizer,
self.max_seq_length)
sub_ent_list.append(spo['subject'])
else:
#complex sample
complex_relation_label = [6, 8, 24, 30, 44]
complex_relation_affi_label = [
7, 9, 25, 26, 27, 31, 45
]
predicate_label = self.spo_conf[
spo['predicate'] + '_' + spo_object]
if predicate_label in complex_relation_affi_label:
subject_sub_tokens = covert_to_tokens(
spo['object']['@value'],
self.tokenizer, self.max_seq_length)
sub_ent_list.append(
spo['object']['@value'])
else:
subject_sub_tokens = covert_to_tokens(
spo['subject'], self.tokenizer,
self.max_seq_length)
sub_ent_list.append(spo['subject'])
object_sub_tokens = covert_to_tokens(
spo['object'][spo_object], self.tokenizer,
self.max_seq_length)
subject_start, object_start = search_spo_index(
tokens, subject_sub_tokens, object_sub_tokens)
if subject_start == -1:
subject_start = search(subject_sub_tokens,
tokens)
if object_start == -1:
object_start = search(object_sub_tokens,
tokens)
if subject_start != -1 and object_start != -1:
s = (subject_start, subject_start +
len(subject_sub_tokens) - 1)
o = (object_start,
object_start + len(object_sub_tokens) - 1,
predicate_label)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
examples.append(
Example(
p_id=p_id,
raw_text=src_data['text'],
context=text_raw,
tok_to_orig_start_index=tok_to_orig_start_index,
tok_to_orig_end_index=tok_to_orig_end_index,
bert_tokens=tokens,
sub_entity_list=sub_ent_list,
gold_answer=src_data['spo_list'],
spoes=spoes))
# print('total gold num is {}'.format(gold_num))
logging.info("{} total size is {} ".format(data_type, len(examples)))
return examples
def _read(self, filename, data_type):
complex_relation_label = [6, 8, 24, 30, 44]
complex_relation_affi_label = [7, 9, 25, 26, 27, 31, 45]
examples = []
with open(filename, 'r') as fr:
p_id = 0
for line in tqdm(fr.readlines()):
p_id += 1
src_data = json.loads(line)
text_raw = src_data['text']
text_raw = text_raw.replace('®', '')
text_raw = text_raw.replace('◆', '')
tokens, tok_to_orig_start_index, tok_to_orig_end_index = covert_to_tokens(
text_raw,
self.tokenizer,
self.max_seq_length,
return_orig_index=True)
tokens = ["[CLS]"] + tokens + ["[SEP]"]
sub_po_dict, sub_ent_list, spo_list = dict(), list(), list()
spoes = {}
for spo in src_data['spo_list']:
spo_dict = dict()
for spo_object in spo['object'].keys():
if spo['predicate'] in self.spo_conf:
label = spo['predicate']
else:
label = spo['predicate'] + '_' + spo_object
spo_dict[
self.spo_conf[label]] = spo['object'][spo_object]
for spo_object in spo['object'].keys():
# assign relation label
if spo['predicate'] in self.spo_conf:
# simple relation
predicate_label = self.spo_conf[spo['predicate']]
subject_sub_tokens = covert_to_tokens(
spo['subject'], self.tokenizer,
self.max_seq_length)
object_sub_tokens = covert_to_tokens(
spo['object']['@value'], self.tokenizer,
self.max_seq_length)
sub_ent_list.append(spo['subject'])
else:
# complex relation
predicate_label = self.spo_conf[spo['predicate'] +
'_' + spo_object]
if predicate_label in complex_relation_affi_label:
subject_sub_tokens = covert_to_tokens(
spo['object']['@value'], self.tokenizer,
self.max_seq_length)
sub_ent_list.append(spo['object']['@value'])
else:
subject_sub_tokens = covert_to_tokens(
spo['subject'], self.tokenizer,
self.max_seq_length)
sub_ent_list.append(spo['subject'])
object_sub_tokens = covert_to_tokens(
spo['object'][spo_object], self.tokenizer,
self.max_seq_length)
subject_start, object_start = search_spo_index(
tokens, subject_sub_tokens, object_sub_tokens)
if subject_start == -1:
subject_start = search(subject_sub_tokens, tokens)
if object_start == -1:
object_start = search(object_sub_tokens, tokens)
if subject_start != -1 and object_start != -1:
s = (subject_start,
subject_start + len(subject_sub_tokens) - 1)
o = (object_start,
object_start + len(object_sub_tokens) - 1,
predicate_label)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
examples.append(
Example(p_id=p_id,
context=text_raw,
tok_to_orig_start_index=tok_to_orig_start_index,
tok_to_orig_end_index=tok_to_orig_end_index,
bert_tokens=tokens,
sub_entity_list=sub_ent_list,
gold_answer=src_data['spo_list'],
spoes=spoes))
if data_type == 'train':
flag = False
for s, o in spoes.items():
for (o1, o2, p) in o:
if p in complex_relation_affi_label:
flag = True
continue
if not flag:
continue
tmp_spoes = {}
tmp_spoes[s] = spoes[s]
examples.append(
Example(
p_id=p_id,
context=text_raw,
tok_to_orig_start_index=tok_to_orig_start_index,
tok_to_orig_end_index=tok_to_orig_end_index,
bert_tokens=tokens,
sub_entity_list=sub_ent_list,
gold_answer=src_data['spo_list'],
spoes=tmp_spoes))
# else:
# examples.append(
# Example(
# p_id=p_id,
# context=text_raw,
# tok_to_orig_start_index=tok_to_orig_start_index,
# tok_to_orig_end_index=tok_to_orig_end_index,
# bert_tokens=tokens,
# sub_entity_list=sub_ent_list,
# gold_answer=src_data['spo_list'],
# spoes=None
# ))
# print('total gold num is {}'.format(gold_num))
logging.info("{} total size is {} ".format(data_type, len(examples)))
return examples
def collate(examples):
p_ids, examples = zip(*examples)
p_ids = torch.tensor([p_id for p_id in p_ids], dtype=torch.long)
batch_char_ids, batch_word_ids = [], []
batch_token_type_ids, batch_subject_labels, batch_subject_ids, batch_object_labels = [], [], [], []
for example in examples:
# todo maxlen
char_ids = [
self.char2idx.get(char, 1) for char in example.context
]
word_ids = [
self.word2idx.get(word, 0) for word in example.text_word
for _ in word
]
if len(char_ids) != len(word_ids):
print(example.context)
print(char_ids)
print(len(char_ids))
print(example.text_word)
print(word_ids)
print(len(word_ids))
assert len(char_ids) == len(word_ids)
char_ids = char_ids[:self.max_len]
word_ids = word_ids[:self.max_len]
# example.context = example.context[:self.max_len]
if self.is_train:
spoes = {}
for s, p, o in example.gold_answer:
s = [self.char2idx.get(s_, 1) for s_ in s]
# p = BAIDU_RELATION[p]
o = [self.char2idx.get(o_, 1) for o_ in o]
s_idx = search(s, char_ids)
o_idx = search(o, char_ids)
if s_idx != -1 and o_idx != -1:
s = (s_idx, s_idx + len(s) - 1)
o = (o_idx, o_idx + len(o) - 1, p)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
if spoes:
# subject标签
token_type_ids = np.zeros(len(char_ids), dtype=np.long)
subject_labels = np.zeros(len(char_ids), dtype=np.int)
for s in spoes:
subject_labels[s[0]] = BAIDU_ENTITY['B']
for index in range(s[0] + 1, s[1] + 1):
subject_labels[index] = BAIDU_ENTITY['I']
# 随机选一个subject
subject_ids = random.choice(list(spoes.keys()))
token_type_ids[subject_ids[0]:subject_ids[1] + 1] = 1
# 对应的object标签
object_labels = np.zeros(len(char_ids), dtype=np.int)
for o in spoes.get(subject_ids, []):
object_labels[o[0]] = BAIDU_BIES['B' + '-' + o[2]]
for index in range(o[0] + 1, o[1] + 1):
object_labels[index] = BAIDU_BIES['I' + '-' +
o[2]]
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_token_type_ids.append(token_type_ids)
batch_subject_labels.append(subject_labels)
batch_subject_ids.append(subject_ids)
batch_object_labels.append(object_labels)
else:
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_char_ids = sequence_padding(batch_char_ids, is_float=False)
batch_word_ids = sequence_padding(batch_word_ids, is_float=False)
if not self.is_train:
return p_ids, batch_char_ids, batch_word_ids
else:
batch_token_type_ids = sequence_padding(batch_token_type_ids,
is_float=False)
batch_subject_ids = torch.tensor(batch_subject_ids)
batch_subject_labels = sequence_padding(batch_subject_labels,
is_float=False)
batch_object_labels = sequence_padding(batch_object_labels,
is_float=False)
return batch_char_ids, batch_word_ids, batch_token_type_ids, batch_subject_ids, batch_subject_labels, batch_object_labels
def _read(self, filename, data_type):
examples = []
with codecs.open(filename, 'r') as f:
gold_num = 0
p_id = 0
for line in tqdm(f):
p_id += 1
data_json = json.loads(line.strip())
text_raw = data_json['text'].lower()
sub_text = []
buff = ""
for char in text_raw:
if chineseandpunctuationextractor.is_chinese_or_punct(char):
if buff != "":
sub_text.append(buff)
buff = ""
sub_text.append(char)
else:
buff += char
if buff != "":
sub_text.append(buff)
# todo 注意：推断的时候应该移除CLS 和 SEP
tok_to_orig_start_index = []
tok_to_orig_end_index = []
tokens = []
text_tmp = ''
for (i, token) in enumerate(sub_text):
sub_tokens = self.tokenizer.tokenize(token) if token != ' ' else []
text_tmp += token
for sub_token in sub_tokens:
tok_to_orig_start_index.append(len(text_tmp) - len(token))
tok_to_orig_end_index.append(len(text_tmp) - 1)
tokens.append(sub_token)
if len(tokens) >= self.max_seq_length - 2:
break
else:
continue
break
tokens = ["[CLS]"] + tokens + ["[SEP]"]
sub_po_dict, sub_ent_list, spo_list = dict(), list(), list()
spoes = {}
for spo in data_json['spo_list']:
for spo_object in spo['object'].keys():
# assign relation label
if spo['predicate'] in self.spo_conf:
# simple relation
predicate_label = self.spo_conf[spo['predicate']]
subject_sub_tokens = self.tokenizer.tokenize(spo['subject'])
object_sub_tokens = self.tokenizer.tokenize(spo['object'][
'@value'])
# todo 补充spo_v2版本时的处理逻辑
sub_ent_list.append(spo['subject'].lower())
spo_list.append((spo['subject'].lower(), spo['predicate'], spo['object']['@value'].lower()))
else:
# complex relation
predicate_label = self.spo_conf[spo['predicate'] + '_' +
spo_object]
subject_sub_tokens = self.tokenizer.tokenize(spo['subject'])
object_sub_tokens = self.tokenizer.tokenize(spo['object'][
spo_object])
subject_start, object_start = search_spo_index(tokens, subject_sub_tokens, object_sub_tokens)
if subject_start == -1:
subject_start = search(subject_sub_tokens, tokens)
if object_start == -1:
object_start = search(object_sub_tokens, tokens)
if subject_start != -1 and object_start != -1:
s = (subject_start, subject_start + len(subject_sub_tokens) - 1)
o = (object_start, object_start + len(object_sub_tokens) - 1, predicate_label)
if s not in spoes:
spoes[s] = []
spoes[s].append(o)
examples.append(
Example(
p_id=p_id,
context=text_raw,
tok_to_orig_start_index=tok_to_orig_start_index,
tok_to_orig_end_index=tok_to_orig_end_index,
bert_tokens=tokens,
sub_entity_list=list(set(sub_ent_list)),
gold_answer=spo_list,
spoes=spoes
)
)
gold_num += len(set(spo_list))
print('total gold num is {}'.format(gold_num))
logging.info("{} total size is {} ".format(data_type, len(examples)))
return examples
def collate(examples):
p_ids, examples = zip(*examples)
p_ids = torch.tensor([p_id for p_id in p_ids], dtype=torch.long)
batch_char_ids, batch_word_ids = [], []
batch_ent_labels, batch_rel_labels = [], []
for example in examples:
# print("example: ", example)
# todo maxlen
char_ids = [
self.char2idx.get(char, 1) for char in example.context
]
# 句子的字序列和词序列，长度不同，为了对齐，一个词中的每个字，都对应了所在词的idx，这样保证了，char_ids和word_ids的长度一样
# 进入模型中，相当与一个词中每个字符都对应了这个词的embedding
word_ids = [
self.word2idx.get(word, 0) for word in example.text_word
for _ in word
]
# word_ids = [self.word2idx.get(word, 0) for word in example.text_word]
if len(char_ids) != len(word_ids):
print(example.context)
print(char_ids)
print(len(char_ids))
print(example.text_word)
print(word_ids)
print(len(word_ids))
assert len(char_ids) == len(word_ids)
char_ids = char_ids[:self.max_len]
word_ids = word_ids[:self.max_len]
example.raw_context = example.context[:self.max_len]
if self.is_train:
rel_labels = []
bio = ['O'] * len(char_ids)
for s, p, o in example.gold_answer:
s = [self.char2idx.get(s_, 1) for s_ in s]
p = BAIDU_RELATION[p]
o = [self.char2idx.get(o_, 1) for o_ in o]
s_idx = search(s, char_ids)
o_idx = search(o, char_ids)
if s_idx != -1 and o_idx != -1:
bio[s_idx] = 'B'
bio[s_idx + 1:s_idx + len(s)] = 'I' * (len(s) - 1)
bio[o_idx] = 'B'
bio[o_idx + 1:o_idx + len(o)] = 'I' * (len(o) - 1)
s = (s_idx, s_idx + len(s) - 1)
o = (o_idx, o_idx + len(o) - 1, p)
rel_labels.append((s[1], o[1], o[2]))
if rel_labels:
ent_labels = np.zeros((len(char_ids)), dtype=np.long)
for index, label_ in enumerate(bio):
ent_labels[index] = BAIDU_ENTITY[label_]
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_ent_labels.append(ent_labels)
batch_rel_labels.append(rel_labels)
else:
batch_char_ids.append(char_ids)
batch_word_ids.append(word_ids)
batch_char_ids = sequence_padding(batch_char_ids, is_float=False)
batch_word_ids = sequence_padding(batch_word_ids, is_float=False)
if not self.is_train:
# print("p_ids: ", p_ids)
# print("batch_char_ids: ", batch_char_ids)
# print("batch_word_ids: ", batch_word_ids)
return p_ids, batch_char_ids, batch_word_ids
else:
batch_ent_labels = sequence_padding(batch_ent_labels,
is_float=False)
batch_rel_labels = select_padding(
batch_char_ids,
batch_rel_labels,
is_float=True,
class_num=len(BAIDU_RELATION))
# print("batch_char_ids: shape=", batch_char_ids.shape, "\n", batch_char_ids)
# print("batch_word_ids: shape=", batch_word_ids.shape, "\n", batch_word_ids)
# print("batch_ent_labels: shape=",batch_ent_labels.shape, '\n', batch_ent_labels)
# print("batch_rel_labels: shape=",batch_rel_labels.shape, "\n", batch_rel_labels)
return batch_char_ids, batch_word_ids, batch_ent_labels, batch_rel_labels
