| 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") |
