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illegal_entity_boundary.py
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illegal_entity_boundary.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# file: illegal_entity_boundary.py
from transformers import AutoTokenizer
def load_dataexamples(file_path, ):
with open(file_path, "r") as f:
datalines = f.readlines()
sentence_collections = []
sentence_label_collections = []
word_collections = []
word_label_collections = []
for data_item in datalines:
data_item = data_item.strip()
if len(data_item) != 0:
word, label = tuple(data_item.split(" "))
word_collections.append(word)
word_label_collections.append(label)
else:
sentence_collections.append(word_collections)
sentence_label_collections.append(word_label_collections)
word_collections = []
word_label_collections = []
return sentence_collections, sentence_label_collections
def find_data_instance(file_path, search_string):
sentence_collections, sentence_label_collections = load_dataexamples(file_path)
for sentence_lst, label_lst in zip(sentence_collections, sentence_label_collections):
sentence_str = "".join(sentence_lst)
if search_string in sentence_str:
print(sentence_str)
print("-"*10)
print(sentence_lst)
print(label_lst)
print("=*"*10)
def find_illegal_entity(query, context_tokens, labels, model_path, is_chinese=True, do_lower_case=True):
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, do_lower_case=do_lower_case)
if is_chinese:
context = "".join(context_tokens)
else:
context = " ".join(context_tokens)
start_positions = []
end_positions = []
origin_tokens = context_tokens
print("check labels in ")
print(len(origin_tokens))
print(len(labels))
for label_idx, label_item in enumerate(labels):
if "B-" in label_item:
start_positions.append(label_idx)
if "S-" in label_item:
end_positions.append(label_idx)
start_positions.append(label_idx)
if "E-" in label_item:
end_positions.append(label_idx)
print("origin entity tokens")
for start_item, end_item in zip(start_positions, end_positions):
print(origin_tokens[start_item: end_item + 1])
query_context_tokens = tokenizer.encode_plus(query, context,
add_special_tokens=True,
max_length=500000,
return_overflowing_tokens=True,
return_token_type_ids=True)
if tokenizer.pad_token_id in query_context_tokens["input_ids"]:
non_padded_ids = query_context_tokens["input_ids"][
: query_context_tokens["input_ids"].index(tokenizer.pad_token_id)]
else:
non_padded_ids = query_context_tokens["input_ids"]
non_pad_tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
first_sep_token = non_pad_tokens.index("[SEP]")
end_sep_token = len(non_pad_tokens) - 1
new_start_positions = []
new_end_positions = []
if len(start_positions) != 0:
for start_index, end_index in zip(start_positions, end_positions):
if is_chinese:
answer_text_span = " ".join(context[start_index: end_index + 1])
else:
answer_text_span = " ".join(context.split(" ")[start_index: end_index + 1])
new_start, new_end = _improve_answer_span(query_context_tokens["input_ids"], first_sep_token, end_sep_token,
tokenizer, answer_text_span)
new_start_positions.append(new_start)
new_end_positions.append(new_end)
else:
new_start_positions = start_positions
new_end_positions = end_positions
# clip out-of-boundary entity positions.
new_start_positions = [start_pos for start_pos in new_start_positions if start_pos < 500000]
new_end_positions = [end_pos for end_pos in new_end_positions if end_pos < 500000]
print("print tokens :")
for start_item, end_item in zip(new_start_positions, new_end_positions):
print(tokenizer.convert_ids_to_tokens(query_context_tokens["input_ids"][start_item: end_item + 1]))
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text, return_subtoken_start=False):
"""Returns tokenized answer spans that better match the annotated answer."""
doc_tokens = [str(tmp) for tmp in doc_tokens]
answer_tokens = tokenizer.encode(orig_answer_text, add_special_tokens=False)
tok_answer_text = " ".join([str(tmp) for tmp in answer_tokens])
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start : (new_end+1)])
if text_span == tok_answer_text:
if not return_subtoken_start:
return (new_start, new_end)
tokens = tokenizer.convert_ids_to_tokens(doc_tokens[new_start: (new_end + 1)])
if "##" not in tokens[-1]:
return (new_start, new_end)
else:
for idx in range(len(tokens)-1, -1, -1):
if "##" not in tokens[idx]:
new_end = new_end - (len(tokens)-1 - idx)
return (new_start, new_end)
return (input_start, input_end)
if __name__ == "__main__":
# file_path = "/data/xiaoya/datasets/ner/msra/train.char.bmes"
# search_string = "美亚股份"
# find_data_instance(file_path, search_string)
#
# print("=%"*20)
# print("check entity boundary")
# print("=&"*20)
print(">>> check for Chinese data example ... ...")
context_tokens = ['１', '美', '亚', '股', '份', '３', '２', '．', '６', '６', '２', '民', '族', '集', '团', '２', '２', '．', '３',
'８', '３', '鲁', '石', '化', 'Ａ', '１', '９', '．', '１', '１', '４', '四', '川', '湖', '山', '１', '７', '．',
'０', '９', '５', '太', '原', '刚', '玉', '１', '０', '．', '５', '８', '１', '咸', '阳', '偏', '转', '１', '６',
'．', '１', '１', '２', '深', '华', '发', 'Ａ', '１', '５', '．', '６', '６', '３', '渝', '开', '发', 'Ａ', '１',
'５', '．', '５', '２', '４', '深', '发', '展', 'Ａ', '１', '３', '．', '８', '９', '５', '深', '纺', '织', 'Ａ',
'１', '３', '．', '２', '２', '１', '太', '极', '实', '业', '２', '３', '．', '２', '２', '２', '友', '好', '集',
'团', '２', '２', '．', '１', '４', '３', '双', '虎', '涂', '料', '２', '０', '．', '２', '０', '４', '新', '潮',
'实', '业', '１', '５', '．', '５', '８', '５', '信', '联', '股', '份', '１', '２', '．', '５', '７', '１', '氯',
'碱', '化', '工', '２', '１', '．', '１', '７', '２', '百', '隆', '股', '份', '１', '５', '．', '６', '４', '３',
'贵', '华', '旅', '业', '１', '５', '．', '１', '５', '４', '南', '洋', '实', '业', '１', '４', '．', '５', '０',
'５', '福', '建', '福', '联', '１', '３', '．', '８', '０']
labels = ['O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O',
'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT',
'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O',
'O', 'B-NT', 'M-NT',
'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O',
'O',
'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O',
'O', 'O', 'O',
'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O',
'O', 'O', 'O',
'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT',
'O', 'O',
'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT',
'E-NT',
'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT',
'M-NT', 'M-NT',
'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O',
'B-NT',
'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O', 'O', 'O', 'B-NT', 'M-NT', 'M-NT', 'E-NT', 'O', 'O', 'O', 'O',
'O']
query = "组织机构"
model_path = "/data/nfsdata/nlp/BERT_BASE_DIR/chinese_L-12_H-768_A-12"
find_illegal_entity(query, context_tokens, labels, model_path, is_chinese=True, do_lower_case=True)
print("$$$$$"*20)
print(">>> check for English data example ... ...")
query = "organization"
context_tokens = ['RUGBY', 'LEAGUE', '-', 'EUROPEAN', 'SUPER', 'LEAGUE', 'RESULTS', '/', 'STANDINGS', '.', 'LONDON',
'1996-08-24', 'Results', 'of', 'European', 'Super', 'League', 'rugby', 'league', 'matches', 'on',
'Saturday', ':', 'Paris', '14', 'Bradford', '27', 'Wigan', '78', 'Workington', '4', 'Standings',
'(', 'tabulated', 'under', 'played', ',', 'won', ',', 'drawn', ',', 'lost', ',', 'points', 'for',
',', 'against', ',', 'total', 'points', ')', ':', 'Wigan', '22', '19', '1', '2', '902', '326',
'39', 'St', 'Helens', '21', '19', '0', '2', '884', '441', '38', 'Bradford', '22', '17', '0', '5',
'767', '409', '34', 'Warrington', '21', '12', '0', '9', '555', '499', '24', 'London', '21', '11',
'1', '9', '555', '462', '23', 'Sheffield', '21', '10', '0', '11', '574', '696', '20', 'Halifax',
'21', '9', '1', '11', '603', '552', '19', 'Castleford', '21', '9', '0', '12', '548', '543', '18',
'Oldham', '21', '8', '1', '12', '439', '656', '17', 'Leeds', '21', '6', '0', '15', '531', '681',
'12', 'Paris', '22', '3', '1', '18', '398', '795', '7', 'Workington', '22', '2', '1', '19', '325',
'1021', '5']
labels = ['B-MISC', 'E-MISC', 'O', 'B-MISC', 'I-MISC', 'E-MISC', 'O', 'O', 'O', 'O', 'S-LOC', 'O', 'O', 'O',
'B-MISC', 'I-MISC', 'E-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'S-ORG', 'O', 'S-ORG', 'O',
'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O',
'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'E-ORG', 'O', 'O', 'O', 'O', 'O', 'O',
'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O',
'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O',
'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O',
'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'S-ORG', 'O', 'O',
'O', 'O', 'O', 'O', 'O']
model_path = "/data/xiaoya/models/bert_cased_large"
find_illegal_entity(query, context_tokens, labels, model_path, is_chinese=False, do_lower_case=False)