| import json |
| import os |
| from collections import defaultdict |
| import random |
|
|
| from transformers import AutoTokenizer |
|
|
|
|
| def add_marker_tokens(tokenizer, ner_labels): |
| new_tokens = ['<START>', '<END>'] |
| for label in ner_labels: |
| new_tokens.append('<START=%s>'%label) |
| new_tokens.append('<END=%s>'%label) |
| tokenizer.add_tokens(new_tokens) |
| print('# vocab after adding markers: %d'%len(tokenizer)) |
|
|
|
|
| def tokenize_sentences(ext, tokenizer, special_tokens, rel_file): |
| rel_indices = {} |
| arg_indices = {} |
| label_ids = [] |
|
|
| def get_special_token(w): |
| if w not in special_tokens: |
| special_tokens[w] = ('<' + w + '>').lower() |
| return special_tokens[w] |
|
|
| cls = tokenizer.cls_token |
| sep = tokenizer.sep_token |
| wordpiece_tokens = [cls] |
| wordpiece_tokens_index = [] |
| cur_index = len(wordpiece_tokens) |
|
|
| Argument_START_NER = get_special_token("START=Argument") |
| Argument_END_NER = get_special_token("END=Argument") |
| Relation_START_NER = get_special_token("START=Relation") |
| Relation_END_NER = get_special_token("END=Relation") |
|
|
| ent2offset = {} |
| for ent in ext['entityMentions']: |
| ent2offset[ent['emId']] = ent['span_ids'] |
|
|
| argument_start_ids = [] |
| argument_end_ids = [] |
| relation_start_ids = [] |
| |
| relation_end_ids = [] |
| entity_set = set() |
| relation2entity = defaultdict(set) |
| for rel in ext['relationMentions']: |
| relation_span = ent2offset[rel['arg1']['emId']] |
| relation_start_ids.append(relation_span[0]) |
| relation_end_ids.append(relation_span[-1]) |
| argument_span = ent2offset[rel['arg2']['emId']] |
| argument_start_ids.append(argument_span[0]) |
| argument_end_ids.append(argument_span[-1]) |
| label_ids.append(rel_file["id"][rel['label']]) |
| |
| relation2entity[relation_start_ids[-1]].add(argument_start_ids[-1]) |
| entity_set.add(argument_start_ids[-1]) |
|
|
| for i, token in enumerate(ext['sentence'].split(' ')): |
| if i in relation_start_ids: |
| rel_indices[i] = len(wordpiece_tokens) |
| wordpiece_tokens.append(Relation_START_NER) |
| wordpiece_tokens_index.append([cur_index, cur_index + 1]) |
| cur_index += 1 |
| if i in argument_start_ids: |
| arg_indices[i] = len(wordpiece_tokens) |
| wordpiece_tokens.append(Argument_START_NER) |
| wordpiece_tokens_index.append([cur_index, cur_index + 1]) |
| cur_index += 1 |
|
|
| 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) |
|
|
| if i in relation_end_ids: |
| wordpiece_tokens.append(Relation_END_NER) |
| wordpiece_tokens_index.append([cur_index, cur_index + 1]) |
| cur_index += 1 |
| if i in argument_end_ids: |
| wordpiece_tokens.append(Argument_END_NER) |
| wordpiece_tokens_index.append([cur_index, cur_index + 1]) |
| cur_index += 1 |
|
|
| wordpiece_tokens.append(sep) |
| wordpiece_segment_ids = [1] * (len(wordpiece_tokens)) |
| assert len(argument_start_ids) == len(relation_start_ids) |
| assert len(argument_start_ids) == len(label_ids) |
|
|
| |
| for rel, args in relation2entity.items(): |
| negative_args = list(entity_set.difference(args)) |
| for i in range(len(negative_args) // 3): |
| arg_index = random.randint(0, len(negative_args) - 1) |
| relation_start_ids.append(rel) |
| argument_start_ids.append(negative_args[arg_index]) |
| label_ids.append(0) |
|
|
| return { |
| 'sentId': ext['sentId'], |
| 'sentText': ext['sentence'], |
| 'entityMentions': ext['entityMentions'], |
| 'relationMentions': ext['relationMentions'], |
| 'extractionMentions': ext['extractionMentions'], |
| 'labelIds': label_ids, |
| 'relationIds': [rel_indices[r] for r in relation_start_ids], |
| 'argumentIds': [arg_indices[a] for a in argument_start_ids], |
| '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, rel_file, target_file, pretrained_model): |
| extractions_list = [] |
| auto_tokenizer = AutoTokenizer.from_pretrained(pretrained_model) |
| print("Load {} tokenizer successfully.".format(pretrained_model)) |
|
|
| rel_id_file = json.load(open(rel_file, 'r', encoding='utf-8')) |
| add_marker_tokens(auto_tokenizer, rel_id_file["entity_text"]) |
|
|
| if os.path.exists('special_tokens.json'): |
| with open('special_tokens.json', 'r') as f: |
| special_tokens = json.load(f) |
| else: |
| raise FileNotFoundError |
|
|
| 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, special_tokens, rel_id_file) |
| extractions_list.append(ext_dict) |
| fout.write(json.dumps(ext_dict)) |
| fout.write('\n') |
|
|
| |
| random.seed(100) |
| 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, "train.json") |
| write_dataset_to_file(dev_set, "devs.json") |
| write_dataset_to_file(test_set, "test.json") |
|
|
|
|
| if __name__ == '__main__': |
| process("../benchmark.json", "rel_file.json", "relation_model_data.json", "bert-base-uncased") |
