def convert_texts_to_ids(self, batch_text):
"""将一个batch的明文text转成id
:param batch_text:
:return:
"""
src_ids = []
for text in batch_text:
if self.field_config.need_convert:
tokens = self.tokenizer.tokenize(text)
src_id = self.tokenizer.convert_tokens_to_ids(tokens)
else:
src_id = text.split(" ")
# 加上截断策略
if len(src_id) > self.field_config.max_seq_len:
src_id = truncation_words(src_id, self.field_config.max_seq_len, self.field_config.truncation_type)
src_ids.append(src_id)
return_list = []
padded_ids, batch_seq_lens = pad_batch_data(src_ids,
pad_idx=self.field_config.padding_id,
return_input_mask=False,
return_seq_lens=True)
return_list.append(padded_ids)
return_list.append(batch_seq_lens)
return return_list
def convert_texts_to_ids(self, batch_text):
"""将一个batch的明文text转成id
:param batch_text:
:return:
"""
src_ids = []
for text in batch_text:
if self.field_config.need_convert:
tokens = self.tokenizer.tokenize(text)
src_id = self.tokenizer.convert_tokens_to_ids(tokens)
else:
if isinstance(text, str):
src_id = text.split(" ")
src_id = [int(i) for i in text]
# 加上截断策略
if len(src_id) > self.field_config.max_seq_len - 2:
src_id = truncation_words(src_id, self.field_config.max_seq_len - 2, self.field_config.truncation_type)
unk_id = self.tokenizer.vocabulary.vocab_dict[self.field_config.tokenizer_info["unk_token"]]
src_id.insert(0, unk_id)
src_id.append(unk_id)
src_ids.append(src_id)
return_list = []
padded_ids, mask_ids, batch_seq_lens = pad_batch_data(src_ids,
pad_idx=self.field_config.padding_id,
return_input_mask=True,
return_seq_lens=True)
return_list.append(padded_ids)
return_list.append(mask_ids)
return_list.append(batch_seq_lens)
return return_list
def convert_texts_to_ids(self, batch_text):
""" 明文序列化
:return: id_list
"""
src_ids = []
for text in batch_text:
if self.tokenizer and self.field_config.need_convert:
tokens = self.tokenizer.tokenize(text)
src_id = self.tokenizer.convert_tokens_to_ids(tokens)
else:
src_id = text.split(" ")
# 加上截断策略
if len(src_id) > self.field_config.max_seq_len:
src_id = truncation_words(src_id,
self.field_config.max_seq_len,
self.field_config.truncation_type)
src_ids.append(src_id)
data_type = "int64" if self.field_config.data_type == DataShape.INT else "float32"
padded_ids, batch_seq_lens = pad_batch_data(
src_ids,
insts_data_type=data_type,
pad_idx=self.field_config.padding_id,
return_input_mask=False,
return_seq_lens=True)
return_list = []
return_list.append(padded_ids)
return_list.append(batch_seq_lens)
return return_list
def serialize_batch_records(self, batch_records):
"""pad batch records"""
batch_token_ids = [record.token_ids for record in batch_records]
batch_text_type_ids = [
record.text_type_ids for record in batch_records
]
batch_position_ids = [record.position_ids for record in batch_records]
batch_task_ids = [record.task_ids for record in batch_records]
if "predict" not in self.name:
batch_labels = [record.label_id for record in batch_records]
if self.is_classify:
batch_labels = np.array(batch_labels).astype("int64").reshape(
[-1, 1])
elif self.is_regression:
batch_labels = np.array(batch_labels).astype(
"float32").reshape([-1, 1])
else:
if self.is_classify:
batch_labels = np.array([]).astype("int64").reshape([-1, 1])
elif self.is_regression:
batch_labels = np.array([]).astype("float32").reshape([-1, 1])
if batch_records[0].qid:
batch_qids = [record.qid for record in batch_records]
batch_qids = np.array(batch_qids).astype("int64").reshape([-1, 1])
else:
batch_qids = np.array([]).astype("int64").reshape([-1, 1])
# padding
padded_token_ids, input_mask = pad_batch_data(batch_token_ids,
pad_idx=self.pad_id,
return_input_mask=True)
padded_text_type_ids = pad_batch_data(batch_text_type_ids,
pad_idx=self.pad_id)
padded_position_ids = pad_batch_data(batch_position_ids,
pad_idx=self.pad_id)
padded_task_ids = pad_batch_data(batch_task_ids, pad_idx=0)
return_list = [
padded_token_ids, padded_text_type_ids, padded_position_ids,
padded_task_ids, input_mask, batch_labels, batch_qids
]
return return_list
def convert_texts_to_ids(self, batch_text):
"""将一个batch的明文text转成id
:param batch_text:
:return:
"""
src_ids = []
position_ids = []
task_ids = []
sentence_ids = []
for text in batch_text:
if self.field_config.need_convert:
tokens_text = self.tokenizer.tokenize(text)
# 加上截断策略
if len(tokens_text) > self.field_config.max_seq_len - 2:
tokens_text = truncation_words(
tokens_text, self.field_config.max_seq_len - 2,
self.field_config.truncation_type)
tokens = []
tokens.append("[CLS]")
for token in tokens_text:
tokens.append(token)
tokens.append("[SEP]")
src_id = self.tokenizer.convert_tokens_to_ids(tokens)
else:
if isinstance(text, str):
src_id = text.split(" ")
src_id = [int(i) for i in text]
if len(src_id) > self.field_config.max_seq_len - 2:
src_id = truncation_words(
src_id, self.field_config.max_seq_len - 2,
self.field_config.truncation_type)
src_id.insert(0, self.tokenizer.covert_token_to_id("[CLS]"))
src_id.append(self.tokenizer.covert_token_to_id("[SEP]"))
src_ids.append(src_id)
pos_id = list(range(len(src_id)))
task_id = [0] * len(src_id)
sentence_id = [0] * len(src_id)
position_ids.append(pos_id)
task_ids.append(task_id)
sentence_ids.append(sentence_id)
return_list = []
padded_ids, input_mask, batch_seq_lens = pad_batch_data(
src_ids,
pad_idx=self.field_config.padding_id,
return_input_mask=True,
return_seq_lens=True)
sent_ids_batch = pad_batch_data(sentence_ids,
pad_idx=self.field_config.padding_id)
pos_ids_batch = pad_batch_data(position_ids,
pad_idx=self.field_config.padding_id)
task_ids_batch = pad_batch_data(task_ids,
pad_idx=self.field_config.padding_id)
return_list.append(padded_ids) # append src_ids
return_list.append(sent_ids_batch) # append sent_ids
return_list.append(pos_ids_batch) # append pos_ids
return_list.append(input_mask) # append mask
return_list.append(task_ids_batch) # append task_ids
return_list.append(batch_seq_lens) # append seq_lens
return return_list
def convert_texts_to_ids(self, batch_text):
"""将一个batch的明文text转成id
:param batch_text:
:return:
"""
src_ids = []
position_ids = []
task_ids = []
sentence_ids = []
batch_text_a, batch_text_b = batch_text
assert len(batch_text_a) == len(batch_text_b)
for text_a, text_b in zip(batch_text_a, batch_text_b):
if self.field_config.need_convert:
tokens_text_a = self.tokenizer.tokenize(text_a)
tokens_text_b = self.tokenizer.tokenize(text_b)
# 加上截断策略
truncate_seq_pair(tokens_text_a, tokens_text_b,
self.field_config.max_seq_len - 3)
text_a_len, text_b_len = len(tokens_text_a), len(tokens_text_b)
tokens_text = tokens_text_a + ["[SEP]"] + tokens_text_b
tokens = []
tokens.append("[CLS]")
for token in tokens_text:
tokens.append(token)
tokens.append("[SEP]")
src_id = self.tokenizer.convert_tokens_to_ids(tokens)
else:
src_a_id = text_a.split(" ")
src_b_id = text_b.split(" ")
truncate_seq_pair(src_a_id, src_b_id,
self.field_config.max_seq_len - 3)
text_a_len, text_b_len = len(src_a_id), len(src_b_id)
src_id = src_a_id + ["[SEP]"] + src_b_id
if len(src_id) > self.field_config.max_seq_len - 2:
src_id = truncation_words(
src_id, self.field_config.max_seq_len - 2,
self.field_config.truncation_type)
src_id.insert(0,
self.tokenizer.covert_token_to_id("[CLS]"))
src_id.append(self.tokenizer.covert_token_to_id("[SEP]"))
src_ids.append(src_id)
pos_id = list(range(len(src_id)))
task_id = [0] * len(src_id)
sentence_id = [0] * (text_a_len + 2) + [1] * (text_b_len + 1)
position_ids.append(pos_id)
task_ids.append(task_id)
sentence_ids.append(sentence_id)
return_list = []
padded_ids, input_mask, batch_seq_lens = pad_batch_data(
src_ids,
pad_idx=self.field_config.padding_id,
return_input_mask=True,
return_seq_lens=True)
sent_ids_batch = pad_batch_data(sentence_ids,
pad_idx=self.field_config.padding_id)
pos_ids_batch = pad_batch_data(position_ids,
pad_idx=self.field_config.padding_id)
task_ids_batch = pad_batch_data(task_ids,
pad_idx=self.field_config.padding_id)
return_list.append(padded_ids) # append src_ids
return_list.append(sent_ids_batch) # append sent_ids
return_list.append(pos_ids_batch) # append pos_ids
return_list.append(input_mask) # append mask
return_list.append(task_ids_batch) # append task_ids
return_list.append(batch_seq_lens) # append seq_lens
return return_list