|
import json |
|
import random |
|
import sys |
|
from transformers import AutoTokenizer |
|
|
|
|
|
def add_joint_label(ext, ent_rel_id): |
|
"""add_joint_label add joint labels for sentences |
|
""" |
|
|
|
none_id = ent_rel_id['None'] |
|
sentence_length = len(ext['sentText'].split(' ')) |
|
label_matrix = [[none_id for j in range(sentence_length)] for i in range(sentence_length)] |
|
ent2offset = {} |
|
for ent in ext['entityMentions']: |
|
ent2offset[ent['emId']] = ent['span_ids'] |
|
try: |
|
for i in ent['span_ids']: |
|
for j in ent['span_ids']: |
|
label_matrix[i][j] = ent_rel_id[ent['label']] |
|
except: |
|
sys.exit(1) |
|
for rel in ext['relationMentions']: |
|
arg1_span = ent2offset[rel['arg1']['emId']] |
|
arg2_span = ent2offset[rel['arg2']['emId']] |
|
|
|
for i in arg1_span: |
|
for j in arg2_span: |
|
|
|
label_matrix[i][j] = ent_rel_id[rel['label']] |
|
label_matrix[j][i] = ent_rel_id[rel['label']] |
|
ext['jointLabelMatrix'] = label_matrix |
|
|
|
|
|
def tokenize_sentences(ext, tokenizer): |
|
cls = tokenizer.cls_token |
|
sep = tokenizer.sep_token |
|
wordpiece_tokens = [cls] |
|
|
|
wordpiece_tokens_index = [] |
|
cur_index = len(wordpiece_tokens) |
|
for token in ext['sentence'].split(' '): |
|
tokenized_token = list(tokenizer.tokenize(token)) |
|
wordpiece_tokens.extend(tokenized_token) |
|
wordpiece_tokens_index.append([cur_index, cur_index + len(tokenized_token)]) |
|
cur_index += len(tokenized_token) |
|
wordpiece_tokens.append(sep) |
|
|
|
wordpiece_segment_ids = [1] * (len(wordpiece_tokens)) |
|
|
|
return { |
|
'sentId': ext['sentId'], |
|
'sentText': ext['sentence'], |
|
'entityMentions': ext['entityMentions'], |
|
'relationMentions': ext['relationMentions'], |
|
'extractionMentions': ext['extractionMentions'], |
|
'wordpieceSentText': " ".join(wordpiece_tokens), |
|
'wordpieceTokensIndex': wordpiece_tokens_index, |
|
'wordpieceSegmentIds': wordpiece_segment_ids |
|
} |
|
|
|
|
|
def write_dataset_to_file(dataset, dataset_path): |
|
print("dataset: {}, size: {}".format(dataset_path, len(dataset))) |
|
with open(dataset_path, 'w', encoding='utf-8') as fout: |
|
for idx, ext in enumerate(dataset): |
|
fout.write(json.dumps(ext)) |
|
if idx != len(dataset) - 1: |
|
fout.write('\n') |
|
|
|
|
|
def process(source_file, ent_rel_file, target_file, pretrained_model, max_length=50): |
|
extractions_list = [] |
|
auto_tokenizer = AutoTokenizer.from_pretrained(pretrained_model) |
|
print("Load {} tokenizer successfully.".format(pretrained_model)) |
|
|
|
ent_rel_id = json.load(open(ent_rel_file, 'r', encoding='utf-8'))["id"] |
|
|
|
with open(source_file, 'r', encoding='utf-8') as fin, open(target_file, 'w', encoding='utf-8') as fout: |
|
for line in fin: |
|
ext = json.loads(line.strip()) |
|
ext_dict = tokenize_sentences(ext, auto_tokenizer) |
|
add_joint_label(ext_dict, ent_rel_id) |
|
extractions_list.append(ext_dict) |
|
fout.write(json.dumps(ext_dict)) |
|
fout.write('\n') |
|
|
|
|
|
random.shuffle(extractions_list) |
|
train_set = extractions_list[:len(extractions_list) - 700] |
|
dev_set = extractions_list[-700:-200] |
|
test_set = extractions_list[-200:] |
|
write_dataset_to_file(train_set, "joint_model_data_albert/train.json") |
|
write_dataset_to_file(dev_set, "joint_model_data_albert/dev.json") |
|
write_dataset_to_file(test_set, "joint_model_data_albert/test.json") |
|
|
|
|
|
if __name__ == '__main__': |
|
process("../benchmark.json", "ent_rel_file.json", "constituent_model_data.json", "bert-base-uncased") |
