def find_most_relevant_paragraphs(section: TextMap,
attention_vector: FixedVector,
min_len: int = 20,
return_delimiters=True,
threshold=0.45):
_blur = int(HyperParameters.subject_paragraph_attention_blur)
_padding = int(_blur * 2 + 1)
paragraph_attention_vector = smooth_safe(
np.pad(attention_vector, _padding, mode='constant'),
_blur)[_padding:-_padding]
paragraph_attention_vector = relu(paragraph_attention_vector, threshold)
top_indices = [
i for i, v in enumerate(paragraph_attention_vector) if v > 0.00001
]
spans = []
for i in top_indices:
span = section.sentence_at_index(i, return_delimiters)
if min_len is not None and span[1] - span[0] < min_len:
if not span in spans:
spans.append(span)
return spans, paragraph_attention_vector
def test_sentence_at_index_return_delimiters(self):
tm = TextMap('стороны Заключили\n договор ПРЕДМЕТ \nДОГОВОРА')
for i in range(len(tm)):
print(i, tm[i])
bounds = tm.sentence_at_index(0)
print(bounds)
print(tm.text_range(bounds))
for i in range(0, 3):
bounds = tm.sentence_at_index(i)
self.assertEqual('стороны Заключили\n', tm.text_range(bounds), str(i))
for i in range(3, 5):
bounds = tm.sentence_at_index(i)
self.assertEqual('договор ПРЕДМЕТ \n', tm.text_range(bounds))
for i in range(6, 7):
bounds = tm.sentence_at_index(i)
self.assertEqual('ДОГОВОРА', tm.text_range(bounds))
