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dataloader.py
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dataloader.py
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import json
import string
from tqdm import tqdm
class SentimentIntrasentenceLoader(object):
def __init__(self, tokenizer, max_seq_length=None, pad_to_max_length=False, input_file="../../data/bias.json"):
stereoset = StereoSet(input_file)
clusters = stereoset.get_intrasentence_examples()
self.tokenizer = tokenizer
self.sentences = []
self.MASK_TOKEN = self.tokenizer.mask_token
self.max_seq_length = max_seq_length
self.pad_to_max_length = pad_to_max_length
if tokenizer.__class__.__name__=="XLNetTokenizer":
self.prepend_text = """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> """
for cluster in clusters:
for sentence in cluster.sentences:
new_sentence = cluster.context.replace("BLANK", sentence.template_word)
self.sentences.append((new_sentence, sentence.ID))
def __len__(self):
return len(self.sentences)
def __getitem__(self, idx):
sentence, sentence_id = self.sentences[idx]
if self.tokenizer.__class__.__name__=="XLNetTokenizer":
text = self.prepend_text
text_pair = sentence
else:
text = sentence
text_pair = None
tokens_dict = self.tokenizer.encode_plus(text, text_pair=text_pair, add_special_tokens=True, max_length=self.max_seq_length, \
pad_to_max_length=self.pad_to_max_length, return_token_type_ids=True, return_attention_mask=True, \
return_overflowing_tokens=False, return_special_tokens_mask=False, return_tensors="pt")
input_ids = tokens_dict['input_ids']
attention_mask = tokens_dict['attention_mask']
token_type_ids = tokens_dict['token_type_ids']
return sentence_id, input_ids, attention_mask, token_type_ids
class IntrasentenceLoader(object):
def __init__(self, tokenizer, max_seq_length=None, pad_to_max_length=False, input_file="../../data/bias.json"):
stereoset = StereoSet(input_file)
clusters = stereoset.get_intrasentence_examples()
self.tokenizer = tokenizer
self.sentences = []
self.MASK_TOKEN = self.tokenizer.mask_token
self.max_seq_length = max_seq_length
self.pad_to_max_length = pad_to_max_length
if tokenizer.__class__.__name__=="XLNetTokenizer":
self.prepend_text = """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> """
for cluster in clusters:
for sentence in cluster.sentences:
insertion_tokens = self.tokenizer.encode(sentence.template_word, add_special_tokens=False)
for idx in range(len(insertion_tokens)):
insertion = self.tokenizer.decode(insertion_tokens[:idx])
insertion_string = f"{insertion}{self.MASK_TOKEN}"
new_sentence = cluster.context.replace("BLANK", insertion_string)
# print(new_sentence, self.tokenizer.decode([insertion_tokens[idx]]))
next_token = insertion_tokens[idx]
self.sentences.append((new_sentence, sentence.ID, next_token))
def __len__(self):
return len(self.sentences)
def __getitem__(self, idx):
sentence, sentence_id, next_token = self.sentences[idx]
if self.tokenizer.__class__.__name__=="XLNetTokenizer":
text = self.prepend_text
text_pair = sentence
else:
text = sentence
text_pair = None
tokens_dict = self.tokenizer.encode_plus(text, text_pair=text_pair, add_special_tokens=True, max_length=self.max_seq_length, \
pad_to_max_length=self.pad_to_max_length, return_token_type_ids=True, return_attention_mask=True, \
return_overflowing_tokens=False, return_special_tokens_mask=False)
input_ids = tokens_dict['input_ids']
attention_mask = tokens_dict['attention_mask']
token_type_ids = tokens_dict['token_type_ids']
return sentence_id, next_token, input_ids, attention_mask, token_type_ids
class StereoSet(object):
def __init__(self, location, json_obj=None):
"""
Instantiates the StereoSet object.
Parameters
----------
location (string): location of the StereoSet.json file.
"""
if json_obj==None:
with open(location, "r") as f:
self.json = json.load(f)
else:
self.json = json_obj
self.version = self.json['version']
self.intrasentence_examples = self.__create_intrasentence_examples__(
self.json['data']['intrasentence'])
self.intersentence_examples = self.__create_intersentence_examples__(
self.json['data']['intersentence'])
def __create_intrasentence_examples__(self, examples):
created_examples = []
for example in examples:
sentences = []
for sentence in example['sentences']:
labels = []
for label in sentence['labels']:
labels.append(Label(**label))
sentence_obj = Sentence(
sentence['id'], sentence['sentence'], labels, sentence['gold_label'])
word_idx = None
for idx, word in enumerate(example['context'].split(" ")):
if "BLANK" in word:
word_idx = idx
if word_idx is None:
raise Exception("No blank word found.")
template_word = sentence['sentence'].split(" ")[word_idx]
sentence_obj.template_word = template_word.translate(str.maketrans('', '', string.punctuation))
sentences.append(sentence_obj)
created_example = IntrasentenceExample(
example['id'], example['bias_type'],
example['target'], example['context'], sentences)
created_examples.append(created_example)
return created_examples
def __create_intersentence_examples__(self, examples):
created_examples = []
for example in examples:
sentences = []
for sentence in example['sentences']:
labels = []
for label in sentence['labels']:
labels.append(Label(**label))
sentence = Sentence(
sentence['id'], sentence['sentence'], labels, sentence['gold_label'])
sentences.append(sentence)
created_example = IntersentenceExample(
example['id'], example['bias_type'], example['target'],
example['context'], sentences)
created_examples.append(created_example)
return created_examples
def get_intrasentence_examples(self):
return self.intrasentence_examples
def get_intersentence_examples(self):
return self.intersentence_examples
class Example(object):
def __init__(self, ID, bias_type, target, context, sentences):
"""
A generic example.
Parameters
----------
ID (string): Provides a unique ID for the example.
bias_type (string): Provides a description of the type of bias that is
represented. It must be one of [RACE, RELIGION, GENDER, PROFESSION].
target (string): Provides the word that is being stereotyped.
context (string): Provides the context sentence, if exists, that
sets up the stereotype.
sentences (list): a list of sentences that relate to the target.
"""
self.ID = ID
self.bias_type = bias_type
self.target = target
self.context = context
self.sentences = sentences
def __str__(self):
s = f"Domain: {self.bias_type} - Target: {self.target} \r\n"
s += f"Context: {self.context} \r\n"
for sentence in self.sentences:
s += f"{sentence} \r\n"
return s
class Sentence(object):
def __init__(self, ID, sentence, labels, gold_label):
"""
A generic sentence type that represents a sentence.
Parameters
----------
ID (string): Provides a unique ID for the sentence with respect to the example.
sentence (string): The textual sentence.
labels (list of Label objects): A list of human labels for the sentence.
gold_label (enum): The gold label associated with this sentence,
calculated by the argmax of the labels. This must be one of
[stereotype, anti-stereotype, unrelated, related].
"""
assert type(ID)==str
assert gold_label in ['stereotype', 'anti-stereotype', 'unrelated']
assert isinstance(labels, list)
assert isinstance(labels[0], Label)
self.ID = ID
self.sentence = sentence
self.gold_label = gold_label
self.labels = labels
self.template_word = None
def __str__(self):
return f"{self.gold_label.capitalize()} Sentence: {self.sentence}"
class Label(object):
def __init__(self, human_id, label):
"""
Label, represents a label object for a particular sentence.
Parameters
----------
human_id (string): provides a unique ID for the human that labeled the sentence.
label (enum): provides a label for the sentence. This must be one of
[stereotype, anti-stereotype, unrelated, related].
"""
assert label in ['stereotype',
'anti-stereotype', 'unrelated', 'related']
self.human_id = human_id
self.label = label
class IntrasentenceExample(Example):
def __init__(self, ID, bias_type, target, context, sentences):
"""
Implements the Example class for an intrasentence example.
See Example's docstring for more information.
"""
super(IntrasentenceExample, self).__init__(
ID, bias_type, target, context, sentences)
class IntersentenceExample(Example):
def __init__(self, ID, bias_type, target, context, sentences):
"""
Implements the Example class for an intersentence example.
See Example's docstring for more information.
"""
super(IntersentenceExample, self).__init__(
ID, bias_type, target, context, sentences)