def add_special_input_features(self, input_example: InputExample,
input_features: InputFeatures) -> None:
mask_start = input_features.input_ids.index(
self.wrapper.tokenizer.mask_token_id)
choices = input_example.meta['candidates']
question_idx = input_example.meta['question_idx']
input_features.meta['candidate_token_ids'] = []
input_features.meta['candidate_labels'] = []
input_features.meta['question_idx'] = question_idx
self.original_choices[question_idx] = []
for idx, choice_text in enumerate(choices):
choice_token_ids = get_verbalization_ids(choice_text,
self.wrapper.tokenizer,
force_single_token=False)
choice_label = 1 if choice_text in input_example.meta[
'answers'] else 0
mask_end = mask_start + len(choice_token_ids)
candidate_token_ids = [-100] * len(input_features.input_ids)
candidate_token_ids[mask_start:mask_end] = choice_token_ids
input_features.meta['candidate_token_ids'].append(
candidate_token_ids)
input_features.meta['candidate_labels'].append(choice_label)
self.original_choices[question_idx].append(choice_text)
def add_special_input_features(self, input_example: InputExample, input_features: InputFeatures) -> None:
if self.wrapper.config.wrapper_type == 'sequence_classifier':
return
mask_start = input_features.input_ids.index(self.wrapper.tokenizer.mask_token_id)
for choice in ['choice1', 'choice2']:
choice_text = input_example.meta[choice]
choice_token_ids = get_verbalization_ids(choice_text, self.wrapper.tokenizer, force_single_token=False)
mask_end = mask_start + len(choice_token_ids)
input_features.meta[f'{choice}_token_ids'] = [-100] * len(input_features.input_ids)
input_features.meta[f'{choice}_token_ids'][mask_start:mask_end] = choice_token_ids
def add_special_input_features(self, input_example: InputExample,
input_features: InputFeatures) -> None:
if self.wrapper.config.wrapper_type == 'sequence_classifier':
return
mask_start = input_features.input_ids.index(
self.wrapper.tokenizer.mask_token_id)
if 'choices' in input_example.meta:
choices = [choice for choice in input_example.meta['choices']]
else:
label_list = self.wrapper.config.label_list
choices = [
self.wrapper.preprocessor.pvp.verbalize(label)[0]
for label in label_list
]
input_features.meta['choice_token_ids'] = []
for idx, choice_text in enumerate(choices):
choice_token_ids = get_verbalization_ids(choice_text,
self.wrapper.tokenizer,
force_single_token=False)
mask_end = mask_start + len(choice_token_ids)
candidate_token_ids = [-100] * len(input_features.input_ids)
candidate_token_ids[mask_start:mask_end] = choice_token_ids
input_features.meta['choice_token_ids'].append(candidate_token_ids)
def add_special_input_features(self, input_example: InputExample, input_features: InputFeatures) -> None:
if self.wrapper.config.wrapper_type == 'sequence_classifier':
return
mask_start = input_features.input_ids.index(self.wrapper.tokenizer.mask_token_id)
num_masks = input_features.input_ids.count(self.wrapper.tokenizer.mask_token_id)
mask_end = mask_start + num_masks
target = input_example.meta['span1_text']
input_features.meta['target'] = target
target_token_ids = get_verbalization_ids(target, self.wrapper.tokenizer, force_single_token=False)
input_features.meta['target_token_ids'] = [-100] * len(input_features.input_ids)
# we also predict <pad> tokens at the missing positions
target_token_ids += [self.wrapper.tokenizer.pad_token_id] * (num_masks - len(target_token_ids))
input_features.meta['target_token_ids'][mask_start:mask_end] = target_token_ids
def get_input_features(self,
example: InputExample,
labelled: bool,
priming: bool = False,
**kwargs) -> InputFeatures:
# 获得PVP（模板句子+label mapping）
input_ids, token_type_ids, block_flag = self.pvp.encode(example)
attention_mask = [1] * len(input_ids)
padding_length = self.wrapper.config.max_seq_length - len(input_ids)
if padding_length < 0:
raise ValueError(
f"Maximum sequence length is too small, got {len(input_ids)} input ids"
)
input_ids = input_ids + ([self.wrapper.tokenizer.pad_token_id] *
padding_length) # wordid序列+padding
attention_mask = attention_mask + ([0] * padding_length)
token_type_ids = token_type_ids + ([0] * padding_length)
block_flag = block_flag + ([0] * padding_length)
assert len(input_ids) == self.wrapper.config.max_seq_length
assert len(attention_mask) == self.wrapper.config.max_seq_length
assert len(token_type_ids) == self.wrapper.config.max_seq_length
assert len(block_flag) == self.wrapper.config.max_seq_length
example_label = example.label
example_task = example.task # add by wjn 表示当前样本所属的task
# add by wjn 只有当数字型的label（0,1），可能真实标签是字符串（'0', '1'），因此需要进行转换判断
if example_label not in self.label_map.keys():
if type(example_label) == int:
example_label = str(example_label)
elif type(example_label) == str:
example_label = int(example_label)
label = self.label_map[
example_label] if example.label is not None else -100
task = task_to_id[example_task] # add by wjn 表示当前task对应group内的编号
logits = example.logits if example.logits else [-1]
if labelled:
mlm_labels = self.pvp.get_mask_positions(input_ids)
else:
mlm_labels = [-1] * self.wrapper.config.max_seq_length
return InputFeatures(
guid=int(example.guid.split('-')[1]),
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task=task,
label=label, # [0,1,..]
mlm_labels=mlm_labels, # [-1, -1, .., -1, 1, -1, -1, ...]
logits=logits,
idx=example.idx,
block_flag=block_flag)
def get_input_features(self, example: InputExample,
**kwargs) -> InputFeatures:
inputs = self.wrapper.task_helper.get_sequence_classifier_inputs(
example) if self.wrapper.task_helper else None
if inputs is None:
inputs = self.wrapper.tokenizer.encode_plus(
example.text_a if example.text_a else None,
example.text_b if example.text_b else None,
add_special_tokens=True,
max_length=self.wrapper.config.max_seq_length,
truncation=True,
)
input_ids, token_type_ids = inputs["input_ids"], inputs.get(
"token_type_ids")
attention_mask = [1] * len(input_ids)
padding_length = self.wrapper.config.max_seq_length - len(input_ids)
input_ids = input_ids + ([self.wrapper.tokenizer.pad_token_id] *
padding_length)
attention_mask = attention_mask + ([0] * padding_length)
if not token_type_ids:
token_type_ids = [0] * self.wrapper.config.max_seq_length
else:
token_type_ids = token_type_ids + ([0] * padding_length)
mlm_labels = [-1] * len(input_ids)
assert len(input_ids) == self.wrapper.config.max_seq_length
assert len(attention_mask) == self.wrapper.config.max_seq_length
assert len(token_type_ids) == self.wrapper.config.max_seq_length
label = self.label_map[
example.label] if example.label is not None else -100
logits = example.logits if example.logits else [-1]
return InputFeatures(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
mlm_labels=mlm_labels,
logits=logits,
idx=example.idx,
)
def get_input_features(self,
example: InputExample,
labelled: bool,
priming: bool = False,
**kwargs) -> InputFeatures:
input_ids, token_type_ids, block_flag = self.pvp.encode(example)
attention_mask = [1] * len(input_ids)
padding_length = self.wrapper.config.max_seq_length - len(input_ids)
if padding_length < 0:
raise ValueError(
f"Maximum sequence length is too small, got {len(input_ids)} input ids"
)
input_ids = input_ids + ([self.wrapper.tokenizer.pad_token_id] *
padding_length)
attention_mask = attention_mask + ([0] * padding_length)
token_type_ids = token_type_ids + ([0] * padding_length)
block_flag = block_flag + ([0] * padding_length)
assert len(input_ids) == self.wrapper.config.max_seq_length
assert len(attention_mask) == self.wrapper.config.max_seq_length
assert len(token_type_ids) == self.wrapper.config.max_seq_length
assert len(block_flag) == self.wrapper.config.max_seq_length
label = self.label_map[
example.label] if example.label is not None else -100
logits = example.logits if example.logits else [-1]
if labelled:
mlm_labels = self.pvp.get_mask_positions(input_ids)
else:
mlm_labels = [-1] * self.wrapper.config.max_seq_length
return InputFeatures(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
mlm_labels=mlm_labels,
logits=logits,
idx=example.idx,
block_flag=block_flag)
def add_special_input_features(self, input_example: InputExample,
input_features: InputFeatures) -> None:
input_features.meta['question_idx'] = input_example.meta[
'question_idx']
def get_input_features(self,
example: InputExample,
labelled: bool,
priming: bool = False,
**kwargs) -> InputFeatures:
if priming:
input_ids, token_type_ids = self.pvp.encode(example, priming=True)
priming_data = example.meta[
'priming_data'] # type: List[InputExample]
priming_input_ids = []
for priming_example in priming_data:
pe_input_ids, _ = self.pvp.encode(priming_example,
priming=True,
labeled=True)
priming_input_ids += pe_input_ids
input_ids = priming_input_ids + input_ids
token_type_ids = self.wrapper.tokenizer.create_token_type_ids_from_sequences(
input_ids)
input_ids = self.wrapper.tokenizer.build_inputs_with_special_tokens(
input_ids)
else:
input_ids, token_type_ids = self.pvp.encode(example)
attention_mask = [1] * len(input_ids)
length = len(input_ids)
padding_length = self.wrapper.config.max_seq_length - len(input_ids)
if padding_length < 0:
raise ValueError(
f"Maximum sequence length is too small, got {len(input_ids)} input ids"
)
input_ids = input_ids + ([self.wrapper.tokenizer.pad_token_id] *
padding_length)
attention_mask = attention_mask + ([0] * padding_length)
token_type_ids = token_type_ids + ([0] * padding_length)
assert len(input_ids) == self.wrapper.config.max_seq_length
assert len(attention_mask) == self.wrapper.config.max_seq_length
assert len(token_type_ids) == self.wrapper.config.max_seq_length
label = self.label_map[
example.label] if example.label is not None else -100
logits = example.logits if example.logits else [-1]
if labelled:
mlm_labels = self.pvp.get_mask_positions(input_ids)
if self.wrapper.config.model_type == 'gpt2':
# shift labels to the left by one
mlm_labels.append(mlm_labels.pop(0))
else:
mlm_labels = [-1] * self.wrapper.config.max_seq_length
return InputFeatures(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
mlm_labels=mlm_labels,
logits=logits,
idx=example.idx,
length=length)