def how_many_times_character_appears(self, passage):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.5)
letters_frequency = extract_letters_frequency(text)
if self.limit_classes is not None:
letters_frequency = dict(
filter(lambda entry: entry[1] < self.limit_classes,
letters_frequency.items()))
random_character = np.random.choice(list(letters_frequency.keys()))
answer = letters_frequency[random_character]
start_idx = passage.index(text)
spans = [(start_idx + i, start_idx + i + 1) for i, x in enumerate(text)
if x == random_character]
question = randomize_instances_occurance_question(
string_asked_about=f'\'{random_character}\'',
string_category='character',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='CHAR')
return sample_details
def _how_many_common_template(self, passage, ner_categories,
singular_string_category,
plural_string_category):
order_of_common = np.random.randint(0, 3)
order_name = [
'the most common', 'the second most common',
'the third most common'
][order_of_common]
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=1.00)
ner_and_spans = extract_ner(text, ner_categories)
spans = [(entry[1], entry[2]) for entry in ner_and_spans]
tokens_text = [entry[0] for entry in ner_and_spans]
sorted_tokens_by_frequency = Counter(tokens_text).most_common()
most_common_token = sorted_tokens_by_frequency[order_of_common]
answer = most_common_token[1]
question = randomize_common_question(
string_category=f'{order_name} {singular_string_category}',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='SPAN')
return sample_details
def _select_template(self, passage, pos_to_extract, string_category):
order_of_common = np.random.randint(0, 3)
order_name = [
'the most common', 'the second most common',
'the third most common'
][order_of_common]
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=1.00)
spans, tokens_text = extract_pos(text, pos_to_extract=pos_to_extract)
sorted_tokens_by_frequency = Counter(tokens_text).most_common()
most_common_token = sorted_tokens_by_frequency[order_of_common]
answer = most_common_token[1]
question = randomize_common_question(
string_category=f'{order_name} {string_category}',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='SPAN')
return sample_details
def how_many_nouns(self, passage):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.25)
spans, _ = extract_pos(text, pos_to_extract=['NN'])
answer = len(spans)
question = randomize_total_question(string_category=f'noun',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='SPAN')
return sample_details
def how_many_sentences_in_total(self, passage):
context_scope, sen_index, passage = randomize_scope(passage,
passage_prob=1.0)
sentences = extract_sentences(passage)
spans = [(passage.index(s), passage.index(s) + len(s))
for s in sentences]
answer = len(sentences)
question = randomize_total_question(string_category='sentence',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='SENTENCE')
return sample_details
def how_many_words_in_total(self, passage):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.0)
words = extract_words(text)
start_idx = passage.index(text)
spans = [(start_idx + w.idx, start_idx + w.idx + len(str(w)))
for w in words]
answer = len(words)
question = randomize_total_question(string_category='word',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='WORD')
return sample_details
def how_many_numbers(self, passage):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.5)
start_idx = passage.index(text)
num_words, num_indices = extract_passage_numbers(text)
spans = [(start_idx + idx, start_idx + idx + len(str(w)))
for w, idx in zip(num_words, num_indices)]
answer = len(num_words)
question = randomize_total_question(string_category='number',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='WORD')
return sample_details
def _how_many_template(self, passage, ner_categories,
singular_string_category, plural_string_category):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.85)
ner_and_spans = extract_ner(text, ner_categories)
spans = [(entry[1], entry[2]) for entry in ner_and_spans]
answer = len(spans)
question = randomize_ner_question(
singular_string_category=singular_string_category,
plural_string_category=plural_string_category,
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='SPAN')
return sample_details
def how_many_words_shorter_than(self, passage):
context_scope, sen_index, text = randomize_scope(passage, passage_prob=0.2)
words = extract_words(text)
target_len = np.random.random_integers(5)
target_words = list(filter(lambda tok: len(str(tok[0])) < target_len, words))
start_idx = passage.index(text)
spans = [(start_idx + w.idx, start_idx + w.idx + len(str(w))) for w in target_words]
answer = len(target_words)
question = randomize_total_question(string_category=f'words shorter than {target_len} character',
context_scope=context_scope)
sample_details = dict(
question=question,
answer=answer,
spans=spans,
span_type='WORD'
)
return sample_details
def how_many_title_case_words_in_total(self, passage):
context_scope, sen_index, text = randomize_scope(passage,
passage_prob=0.5)
words = extract_words(text)
title_words = list(
filter(lambda tok: len(tok.text) > 0 and tok.text[0].isupper(),
words))
start_idx = passage.index(text)
spans = [(start_idx + w.idx, start_idx + w.idx + len(str(w)))
for w in title_words]
answer = len(title_words)
question = randomize_total_question(string_category='title case word',
context_scope=context_scope)
sample_details = dict(question=question,
answer=answer,
spans=spans,
span_type='WORD')
return sample_details
def how_many_times_vowels_appears(self, passage):
context_scope, sen_index, text = randomize_scope(passage, passage_prob=0.0)
letters_frequency = extract_letters_frequency(text)
if self.limit_classes is not None:
letters_frequency = dict(filter(lambda entry: entry[1] < self.limit_classes, letters_frequency.items()))
vowel_entries = dict(filter(lambda e: e[0].lower() in ('a', 'e', 'i', 'u', 'o'), letters_frequency.items()))
answer = sum(vowel_entries.values())
start_idx = passage.index(text)
spans = [(start_idx + i, start_idx + i + 1) for i, x in enumerate(text) if x in vowel_entries.keys()]
question = randomize_total_question(string_category='vowel character', context_scope=context_scope)
sample_details = dict(
question=question,
answer=answer,
spans=spans,
span_type='CHAR'
)
return sample_details