├── .DS_Store ├── README.md ├── baseline ├── .DS_Store ├── README.md ├── bert_model.py ├── common │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── instance.cpython-36.pyc │ │ ├── instance.cpython-37.pyc │ │ ├── sentence.cpython-36.pyc │ │ ├── sentence.cpython-37.pyc │ │ ├── span.cpython-36.pyc │ │ └── span.cpython-37.pyc │ ├── instance.py │ └── sentence.py ├── config │ ├── .DS_Store │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── config.cpython-36.pyc │ │ ├── config.cpython-37.pyc │ │ ├── eval.cpython-36.pyc │ │ ├── eval.cpython-37.pyc │ │ ├── reader.cpython-36.pyc │ │ ├── reader.cpython-37.pyc │ │ ├── utils.cpython-36.pyc │ │ └── utils.cpython-37.pyc │ ├── config.py │ ├── eval.py │ ├── reader.py │ └── utils.py ├── model │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── bilstm_encoder.cpython-37.pyc │ │ ├── charbilstm.cpython-37.pyc │ │ ├── linear_crf_inferencer.cpython-36.pyc │ │ ├── linear_crf_inferencer.cpython-37.pyc │ │ └── neuralcrf.cpython-37.pyc │ └── linear_crf_inferencer.py ├── run.sh ├── train_model.py └── utils.py ├── data ├── .DS_Store ├── test │ ├── TestLabel.json │ └── test.txt ├── train.txt ├── train_dict │ ├── .DS_Store │ ├── NUM.txt │ ├── PER.txt │ └── TV.txt └── valid.txt └── submission ├── TestData.json ├── ValidLabel.json ├── YourTeamName.json └── eval.py /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/.DS_Store -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # NLPCC-2020 Shared Task on AutoIE 3 | 4 | ## Leaderboard 5 | 6 | | Rank | TeamName | Organization | F1-score | 7 | | --- | :--- | :---: | ---: | 8 | | 1 | Sophie | 搜狗杭州研究院知识图谱组 | 85.00 | 9 | | 2 | Hair Loss Knight | 美团点评NLP中心 | 84.87 | 10 | | 3 | Hermers | 武汉汉王大数据 | 84.75 | 11 | | 4 | augmented_autoner | PATech | 82.88 | 12 | | 5 | 一只小绵羊 | 北京语言大学 | 82.78 | 13 | | 6 | STAM | 中国科学院信息工程研究所 | 82.50 | 14 | | 7 | BUTAUTOJ | 北京工业大学信息学部 | 80.91 | 15 | | 8 | Circle | 北京林业大学 | 80.80 | 16 | | 9 | yunke_ws | 加拿大皇后大学 | 80.34 | 17 | | 10 | AI surfing | Nanjing University of Posts and Telecommunications | 80.28 | 18 | | 11 | Yulong | 武汉大学 | 79.52 | 19 | | 12 | 小牛队 | 东北大学自然语言处理实验室 | 78.64 | 20 | | 13 | Auto-IE | 北京航空航天大学计算机系实体抽取组 | 75.20 | 21 | | 14 | AutoIE_ISCAS | Institute of Software, Chinese Academy of Sciences | 74.59 | 22 | | 15 | FIGHTING | 大连民族大学 | 69.75 | 23 | | 16 | ENCH | DCD Lab Zhejiang University | 67.70 | 24 | | 17 | BaselineSystem | NLPCC | 63.98 | 25 | 26 | ## Leaderboard without Valid Data 27 | 28 | | Rank | TeamName | Organization | F1-score | 29 | | --- | :--- | :---: | ---: | 30 | | 1 | Hair Loss Knight | 美团点评NLP中心 | 77.32 | 31 | | 2 | yunke_ws | 加拿大皇后大学 | 71.96 | 32 | | 3 | Hermers | 武汉汉王大数据 | 71.86 | 33 | | 4 | augmented_autoner | PATech | 71.70 | 34 | | 5 | ENCH | DCD Lab Zhejiang University | 70.71 | 35 | | 6 | Circle | 北京林业大学 | 68.59 | 36 | | 7 | 小牛队 | 东北大学自然语言处理实验室 | 65.91 | 37 | | 8 | BaselineSystem | NLPCC | 63.98 | 38 | 39 | ## Background 40 | 41 | Entity extraction is the fundamental problem in language technology, and usually utilized as inputs for many downstream tasks, especially dialogue system, question answering etc. Most previous work focus on the scenario that labelled data is provided for interesting entities, however, the categories of entities are hierarchical and cannot be exhausted, the general solution cannot depend on the hypothesis that enough data with label is given. Therefore, how to build IE system for new entity type under low resource is becoming the common problem for both academic and industry. 42 | 43 | ## Task 44 | 45 | The task is to build IE system with Noise and Incomplete annotations. Given a list of entities of specific type and a unlabelled corpus containing these entity types, the task aims to build an IE system which may recognize and extract the interesting entities of given types. 46 | 47 | Note: 48 | 1. entity is a general concept of named entity in this task. Some words without a specific name are also very important for downstream applications, therefore, they are included in this information extraction task 49 | 2. No human annotation and correction are allowed for train and test dataset. 50 | 3. Dev dataset with full label may be used in the training step in any way. 51 | 52 | ## Data 53 | 54 | The corpus are from caption text of YouKu video. Three categories of information are considered in this task, which are TV, person and series. All data are split into 3 datasets for training, developing and testing. 55 | 56 | Train dataset 57 | 1. Unlabelled corpus containing 10000 samples, the entities are labelled by string matching with the given entity lists. 58 | 2. Entity lists with specific category, which may cover around 30% of entities appearing in the unlabelled corpus 59 | 60 | Dev dataset 61 | 1. 1000 samples with full label 62 | 63 | Test dataset 64 | 1. 2000 samples with full label 65 | 66 | ## Baseline 67 | The evaluation provides a baseline system for participants. The solution is based on the paper "Better Modeling of Incomplete Annotations for Named Entity Recognition", please check the readme file in the baseline folder for more detail 68 | 69 | ## Submission & Evaluation 70 | For submission, please write the prediction result into a single file and email it to Xuefeng Yang (杨雪峰) email：ryan@wezhuiyi.com 71 | 72 | The submission file format should be the same as given YourTeamName.json file under Submission folder. To be specific, each line is a json string containing the prediction result of one sample. 73 | 74 | For evaluation. all the system will be evaluated against 2000 test samples with full annotation. Ranking of submissions are based on the f1 score of these test samples. The test dataset includes 2000 real test samples and 8000 mixed samples, the score is only based on the prediction of the real 2000 samples. 75 | 76 | The test dataset will be provided in 2020/05/15, and each team has three oppotunities to submit their results in the week 05/15--05/20. The results are public available in this github page and ranked by the f1 score. 77 | 78 | ## Organizers: 79 | 80 | Xuefeng Yang (ZhuiYi Technology) 81 | email: ryan@wezhuiyi.com 82 | 83 | Benhong Wu (ZhuiYi Technology) 84 | email: wubenhong@wezhuiyi.com 85 | 86 | Zhanming Jie (Singapore University of Technology and Design) 87 | email: zhanming_jie@mymail.sutd.edu.sg 88 | -------------------------------------------------------------------------------- /baseline/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/.DS_Store -------------------------------------------------------------------------------- /baseline/README.md: -------------------------------------------------------------------------------- 1 | This repository is the baseline for the AutoIE evaluation. The solution is based on the work “Better Modeling of Incomplete Annotations for Named Entity Recognition”。 Different from the implementation in the origin work, we add pretrained language model. Please cite it if you use it. 2 | 3 | ### Requirements 4 | * PyTorch >= 1.1 5 | * Python 3 6 | * tqdm(pip install tqdm) 7 | * transformers(pip install transformers) 8 | * numpy >= 1.16 9 | * termcolor >= 1.1(pip install termcolor) 10 | 11 | 12 | ### Data: 13 | * Please copy all the files and folder under data path in the AutoIE repository into the data folder under this repository. 14 | * Data include the "train_dict", "valid", "train". 15 | * Train_dict: including 3 types entity dict: TV, PER and NUM. 16 | 17 | ### Train model: 18 | * 1.Download the bert pretrained model chinese_wwm_ext released by 哈工大讯飞联合实验室. You can down load it in "https://drive.google.com/open?id=1iNeYFhCBJWeUsIlnW_2K6SMwXkM4gLb_" or "https://pan.iflytek.com/link/B9ACE1C9F228A0F42242672EF6CE1721"(password:XHu4). 19 | * 2.Put the model file {"bert_config.json", "pytorch_model.bin", "vocab.txt"} to the folder: "bert-base-chinese-pytorch". 20 | * 3.Run "run.sh" to train model. 21 | 22 | ### Performance: 23 | * f1 = 65.5 on valid set. 24 | 25 | ### Reference 26 | @inproceedings{jie-etal-2019-better, 27 | title = "Better Modeling of Incomplete Annotations for Named Entity Recognition", 28 | author = "Jie, Zhanming and 29 | Xie, Pengjun and 30 | Lu, Wei and 31 | Ding, Ruixue and 32 | Li, Linlin", 33 | booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", 34 | month = jun, 35 | year = "2019", 36 | doi = "10.18653/v1/N19-1079", 37 | pages = "729--734", 38 | } -------------------------------------------------------------------------------- /baseline/bert_model.py: -------------------------------------------------------------------------------- 1 | """ 2 | transformer based models 3 | """ 4 | import torch 5 | from torch import nn 6 | 7 | from transformers import BertPreTrainedModel, BertModel 8 | 9 | from model.linear_crf_inferencer import LinearCRF 10 | from typing import Tuple 11 | 12 | 13 | class BertCRF(BertPreTrainedModel): 14 | def __init__(self, cfig): 15 | super(BertCRF, self).__init__(cfig) 16 | 17 | self.device = cfig.device 18 | self.num_labels = len(cfig.label2idx) 19 | self.bert = BertModel(cfig) 20 | self.dropout = nn.Dropout(cfig.hidden_dropout_prob) 21 | self.classifier = nn.Linear(cfig.hidden_size, len(cfig.label2idx)) 22 | self.inferencer = LinearCRF(cfig) 23 | 24 | self.init_weights() 25 | 26 | def forward(self, input_ids, input_seq_lens=None, annotation_mask=None, labels=None, 27 | attention_mask=None, token_type_ids=None, 28 | position_ids=None, head_mask=None, add_crf=False): 29 | 30 | outputs = self.bert(input_ids, attention_mask=attention_mask, 31 | token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask) 32 | sequence_output = outputs[0] 33 | 34 | sequence_output = self.dropout(sequence_output) # (batch_size, seq_length, hidden_size) 35 | logits = self.classifier(sequence_output) # (batch_size, seq_length, num_labels) 36 | 37 | if labels is not None: 38 | batch_size = input_ids.size(0) 39 | sent_len = input_ids.size(1) # one batch max seq length 40 | maskTemp = torch.arange(1, sent_len + 1, dtype=torch.long).view(1, sent_len).expand(batch_size, sent_len).to(self.device) 41 | mask = torch.le(maskTemp, input_seq_lens.view(batch_size, 1).expand(batch_size, sent_len)).to(self.device) 42 | 43 | unlabed_score, labeled_score = self.inferencer(logits, input_seq_lens, labels, attention_mask) 44 | return unlabed_score - labeled_score 45 | 46 | else: 47 | bestScores, decodeIdx = self.inferencer.decode(logits, input_seq_lens, annotation_mask) 48 | 49 | return bestScores, decodeIdx 50 | 51 | # obsolete 52 | def decode(self, input_ids, input_seq_lens=None, annotation_mask=None, attention_mask=None) -> Tuple[torch.Tensor, torch.Tensor]: 53 | """ 54 | Decode the batch input 55 | :param batchInput: 56 | :return: 57 | """ 58 | features = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=None, position_ids=None, head_mask=None) 59 | features = self.dropout(features) # (batch_size, seq_length, hidden_size) 60 | logits = self.classifier(features) # (batch_size, seq_length, num_labels) 61 | 62 | bestScores, decodeIdx = self.inferencer.decode(logits, input_seq_lens, annotation_mask) 63 | return bestScores, decodeIdx 64 | 65 | 66 | -------------------------------------------------------------------------------- /baseline/common/__init__.py: -------------------------------------------------------------------------------- 1 | from common.instance import Instance 2 | from common.sentence import Sentence -------------------------------------------------------------------------------- /baseline/common/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/instance.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/instance.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/instance.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/instance.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/sentence.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/sentence.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/sentence.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/sentence.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/span.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/span.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/common/__pycache__/span.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/common/__pycache__/span.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/common/instance.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | from common.sentence import Sentence 3 | 4 | 5 | class Instance: 6 | def __init__(self, input: Sentence, output: List[str] = None) -> None: 7 | """ 8 | Constructor for the instance. 9 | :param input: sentence containing the words 10 | :param output: each position has a label list. Because each position has a list of labels. 11 | """ 12 | self.input = input # words 13 | self.output = output # label lists 14 | self.id = None # the numbers 15 | self.marginals = None 16 | self.word_ids = None 17 | self.output_ids = None 18 | self.is_prediction = None 19 | 20 | def set_id(self, id: int): 21 | self.id = id 22 | 23 | def __len__(self) -> int: 24 | return len(self.input) 25 | 26 | -------------------------------------------------------------------------------- /baseline/common/sentence.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | 4 | class Sentence: 5 | """ 6 | The class for the input sentence 7 | """ 8 | def __init__(self, words: List[str], pos_tags:List[str] = None): 9 | """ 10 | :param words: 11 | :param pos_tags: By default, it is not required to have the pos tags, in case you need it/ 12 | """ 13 | self.words = words 14 | self.pos_tags = pos_tags 15 | 16 | def get_words(self): 17 | return self.words 18 | 19 | def __len__(self): # return the len of words ( in list) 20 | return len(self.words) 21 | 22 | 23 | -------------------------------------------------------------------------------- /baseline/config/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/.DS_Store -------------------------------------------------------------------------------- /baseline/config/__init__.py: -------------------------------------------------------------------------------- 1 | from config.config import Config, PAD, START, STOP 2 | from config.eval import Span, evaluate_batch_insts 3 | from config.reader import Reader 4 | from config.utils import log_sum_exp_pytorch, simple_batching, lr_decay, get_optimizer, batching_list_instances -------------------------------------------------------------------------------- /baseline/config/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/config.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/config.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/config.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/config.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/eval.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/eval.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/eval.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/eval.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/reader.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/reader.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/reader.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/reader.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/utils.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/utils.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/config/__pycache__/utils.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/config/__pycache__/utils.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/config/config.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | from common import Instance 3 | import torch 4 | 5 | 6 | START = "" 7 | STOP = "" 8 | PAD = "" 9 | 10 | 11 | class Config: 12 | def __init__(self, args) -> None: 13 | """ 14 | Construct the arguments and some hyperparameters 15 | :param args: 16 | """ 17 | 18 | # Predefined label string. 19 | self.PAD = PAD 20 | self.B = "B-" 21 | self.I = "I-" 22 | self.E = "E-" 23 | self.O = "O" 24 | self.START_TAG = START 25 | self.STOP_TAG = STOP 26 | self.UNK = "" 27 | self.unk_id = -1 28 | 29 | self.seed = args.seed 30 | self.digit2zero = args.digit2zero 31 | 32 | # Data specification 33 | self.dataset = args.dataset 34 | self.train_file = None 35 | self.dev_file = None 36 | self.label2idx = {} 37 | self.idx2labels = [] 38 | self.train_num = args.train_num 39 | self.dev_num = args.dev_num 40 | self.num_folds = 2 # this is the k mention in paper 41 | 42 | # Training hyperparameter 43 | self.model_folder = args.model_folder 44 | self.optimizer = args.optimizer.lower() 45 | self.learning_rate = args.learning_rate 46 | self.momentum = args.momentum 47 | self.l2 = args.l2 48 | self.num_epochs = args.num_epochs 49 | self.use_dev = True 50 | self.batch_size = args.batch_size 51 | self.clip = 5 52 | self.lr_decay = args.lr_decay 53 | self.device = torch.device(args.device) 54 | self.num_outer_iterations = args.num_outer_iterations 55 | 56 | # transformer hyperparameters 57 | self.bert_model_dir = args.bert_model_dir 58 | self.max_len = args.max_len 59 | self.full_finetuning = args.full_finetuning 60 | self.clip_grad = args.clip_grad 61 | 62 | def build_label_idx(self, insts: List[Instance]) -> None: 63 | """ 64 | Build the mapping from label to index and index to labels. 65 | :param insts: list of instances. 66 | :return: 67 | """ 68 | self.label2idx[self.PAD] = len(self.label2idx) # in this case, idx for PAD is always 0, and [START], [STOP] are in the end 69 | self.idx2labels.append(self.PAD) 70 | for inst in insts: 71 | for label in inst.output: 72 | if label not in self.label2idx: 73 | self.idx2labels.append(label) 74 | self.label2idx[label] = len(self.label2idx) 75 | 76 | for inst in insts: 77 | inst.output_ids = [] if inst.output else None 78 | if inst.output: 79 | for label in inst.output: 80 | inst.output_ids.append(self.label2idx[label]) # assign label ids 81 | 82 | self.label2idx[self.START_TAG] = len(self.label2idx) 83 | self.idx2labels.append(self.START_TAG) 84 | self.label2idx[self.STOP_TAG] = len(self.label2idx) 85 | self.idx2labels.append(self.STOP_TAG) 86 | self.label_size = len(self.label2idx) 87 | self.start_label_id = self.label2idx[self.START_TAG] 88 | self.stop_label_id = self.label2idx[self.STOP_TAG] 89 | print("#labels: {}".format(self.label_size)) 90 | print("label 2idx: {}".format(self.label2idx)) 91 | 92 | -------------------------------------------------------------------------------- /baseline/config/eval.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from typing import List 3 | from common import Instance 4 | import torch 5 | 6 | 7 | class Span: 8 | """ 9 | A class of `Span` where we use it during evaluation. 10 | We construct spans for the convenience of evaluation. 11 | """ 12 | def __init__(self, left: int, right: int, type: str): 13 | """ 14 | A span compose of left, right (inclusive) and its entity label. 15 | :param left: 16 | :param right: inclusive. 17 | :param type: 18 | """ 19 | self.left = left 20 | self.right = right 21 | self.type = type 22 | 23 | def __eq__(self, other): 24 | return self.left == other.left and self.right == other.right and self.type == other.type 25 | 26 | def __hash__(self): 27 | return hash((self.left, self.right, self.type)) 28 | 29 | 30 | def evaluate_batch_insts(batch_insts: List[Instance], 31 | batch_pred_ids: torch.Tensor, 32 | batch_gold_ids: torch.LongTensor, 33 | word_seq_lens: torch.LongTensor, 34 | idx2label: List[str]) -> np.ndarray: 35 | """ 36 | Evaluate a batch of instances and handling the padding positions. 37 | :param batch_insts: a batched of instances. 38 | :param batch_pred_ids: Shape: (batch_size, max_length) prediction ids from the viterbi algorithm. 39 | :param batch_gold_ids: Shape: (batch_size, max_length) gold ids. 40 | :param word_seq_lens: Shape: (batch_size) the length for each instance. 41 | :param idx2label: The idx to label mapping. 42 | :return: numpy array containing (number of true positive, number of all positive, number of true positive + number of false negative) 43 | You can also refer as (number of correctly predicted entities, number of entities predicted, number of entities in the dataset) 44 | """ 45 | p = 0 46 | total_entity = 0 47 | total_predict = 0 48 | word_seq_lens = word_seq_lens.tolist() 49 | for idx in range(len(batch_pred_ids)): 50 | length = word_seq_lens[idx] 51 | output = batch_gold_ids[idx][:length].tolist() 52 | prediction = batch_pred_ids[idx][:length].tolist() 53 | prediction = prediction[::-1] 54 | output = [idx2label[l] for l in output] 55 | prediction =[idx2label[l] for l in prediction] 56 | batch_insts[idx].prediction = prediction 57 | # convert to span 58 | output_spans = set() 59 | start = -1 60 | for i in range(len(output)): 61 | if output[i].startswith("B-"): 62 | start = i 63 | if i == len(output) - 1 or output[i + 1].startswith("B-") or output[i + 1].startswith("O"): 64 | output_spans.add(Span(i, i, output[i][2:])) 65 | if output[i].startswith("E-") and start != -1: 66 | end = i 67 | output_spans.add(Span(start, end, output[i][2:])) 68 | predict_spans = set() 69 | for i in range(len(prediction)): 70 | if prediction[i].startswith("B-"): 71 | start = i 72 | if i == len(prediction) - 1 or prediction[i + 1].startswith("B-") or prediction[i + 1].startswith("O"): 73 | predict_spans.add(Span(i, i, prediction[i][2:])) 74 | if prediction[i].startswith("E-") and start != -1: 75 | end = i 76 | predict_spans.add(Span(start, end, prediction[i][2:])) 77 | 78 | total_entity += len(output_spans) 79 | total_predict += len(predict_spans) 80 | p += len(predict_spans.intersection(output_spans)) 81 | 82 | # In case you need the following code for calculating the p/r/f in a batch. 83 | # (When your batch is the complete dataset) 84 | # precision = p * 1.0 / total_predict * 100 if total_predict != 0 else 0 85 | # recall = p * 1.0 / total_entity * 100 if total_entity != 0 else 0 86 | # fscore = 2.0 * precision * recall / (precision + recall) if precision != 0 or recall != 0 else 0 87 | 88 | return np.asarray([p, total_predict, total_entity], dtype=int) 89 | -------------------------------------------------------------------------------- /baseline/config/reader.py: -------------------------------------------------------------------------------- 1 | from tqdm import tqdm 2 | from common import Sentence, Instance 3 | from typing import List 4 | import re 5 | from transformers import BertTokenizer 6 | 7 | bert_model_dir = "bert-base-chinese-pytorch" # change this if needed 8 | tokenizer = BertTokenizer.from_pretrained(bert_model_dir, do_lower_case=True) 9 | 10 | 11 | class Reader: 12 | def __init__(self, digit2zero: bool=True): 13 | """ 14 | Read the dataset into Instance 15 | :param digit2zero: convert the digits into 0, which is a common practice for LSTM-CRF. 16 | """ 17 | self.digit2zero = digit2zero 18 | self.vocab = set() 19 | 20 | def read_txt(self, file: str, number: int = -1) -> List[Instance]: # expected type -> return type 21 | count_0 = 0 22 | print("Reading file: " + file) 23 | insts = [] 24 | with open(file, 'r', encoding='utf-8') as f: 25 | words = [] 26 | labels = [] 27 | for line in tqdm(f.readlines()): 28 | line = line.rstrip() 29 | if line == "": 30 | assert len(words) == len(labels) 31 | inst = Instance(Sentence(words), labels) 32 | inst.set_id(len(insts)) 33 | insts.append(inst) 34 | words = [] 35 | labels = [] 36 | if len(insts) == number: 37 | break 38 | continue 39 | 40 | #for 41 | x = line.split() 42 | if len(x) == 1: 43 | word, label = '&', x[0] # '&' 44 | elif len(x) == 2: 45 | word, label = x[0], x[1] 46 | else: 47 | print(x) 48 | 49 | if self.digit2zero: 50 | word = re.sub('\d', '0', word) # replace all digits with 0. '\d' - unicode decimal digits [0-9] 51 | count_0 += 1 52 | words.append(word) 53 | self.vocab.add(word) 54 | 55 | labels.append(label) 56 | print("numbers being replaced by zero:", count_0) 57 | print("number of sentences: {}".format(len(insts))) 58 | return insts 59 | 60 | 61 | if __name__ == "__main__": 62 | reader = Reader(False) 63 | tmp_file = "data/" + 'ecommerce' + "/train.txt" 64 | trains = reader.read_txt(tmp_file, -1) 65 | 66 | 67 | 68 | -------------------------------------------------------------------------------- /baseline/config/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from typing import List, Tuple 3 | from common import Instance 4 | import torch.optim as optim 5 | import torch.nn as nn 6 | from config import Config 7 | from termcolor import colored 8 | from transformers import BertTokenizer 9 | 10 | bert_model_dir = "bert-base-chinese-pytorch" # change this if needed 11 | tokenizer = BertTokenizer.from_pretrained(bert_model_dir, do_lower_case=True) 12 | 13 | 14 | def log_sum_exp_pytorch(vec: torch.Tensor) -> torch.Tensor: 15 | """ 16 | Calculate the log_sum_exp trick for the tensor. 17 | :param vec: [batchSize * from_label * to_label]. 18 | :return: [batchSize * to_label] 19 | """ 20 | maxScores, idx = torch.max(vec, 1) 21 | maxScores[maxScores == -float("Inf")] = 0 22 | maxScoresExpanded = maxScores.view(vec.shape[0] ,1 , vec.shape[2]).expand(vec.shape[0], vec.shape[1], vec.shape[2]) 23 | return maxScores + torch.log(torch.sum(torch.exp(vec - maxScoresExpanded), 1)) 24 | 25 | 26 | def batching_list_instances(config: Config, insts: List[Instance]): 27 | train_num = len(insts) 28 | batch_size = config.batch_size 29 | total_batch = train_num // batch_size + 1 if train_num % batch_size != 0 else train_num // batch_size 30 | batched_data = [] 31 | for batch_id in range(total_batch): 32 | one_batch_insts = insts[batch_id * batch_size:(batch_id + 1) * batch_size] 33 | batched_data.append(simple_batching(config, one_batch_insts)) 34 | 35 | return batched_data 36 | 37 | 38 | def simple_batching(config, insts: List[Instance]) -> Tuple: 39 | 40 | """ 41 | batching these instances together and return tensors. The seq_tensors for word and char contain their word id and char id. 42 | :return 43 | word_seq_tensor: Shape: (batch_size, max_seq_length) 44 | word_seq_len: Shape: (batch_size), the length of each sentence in a batch. 45 | context_emb_tensor: Shape: (batch_size, max_seq_length, context_emb_size) 46 | char_seq_tensor: Shape: (batch_size, max_seq_len, max_char_seq_len) 47 | char_seq_len: Shape: (batch_size, max_seq_len), 48 | label_seq_tensor: Shape: (batch_size, max_seq_length) 49 | annotation_mask: Shape (batch_size, max_seq_length, label_size) 50 | """ 51 | batch_size = len(insts) 52 | batch_data = insts 53 | label_size = config.label_size 54 | 55 | # transformer, convert tokens to ids 56 | for idx in range(batch_size): 57 | batch_data[idx].word_ids = tokenizer.convert_tokens_to_ids(batch_data[idx].input.words) 58 | 59 | # probably no need to sort because we will sort them in the model instead. 60 | word_seq_len = torch.LongTensor(list(map(lambda inst: len(inst.input.words), batch_data))) 61 | max_seq_len = word_seq_len.max() 62 | max_allowed_len = min(max_seq_len, config.max_len) 63 | 64 | word_seq_tensor = torch.zeros((batch_size, max_allowed_len), dtype=torch.long) 65 | label_seq_tensor = torch.zeros((batch_size, max_allowed_len), dtype=torch.long) 66 | 67 | annotation_mask = None 68 | if batch_data[0].is_prediction is not None: 69 | annotation_mask = torch.zeros((batch_size, max_allowed_len, label_size), dtype=torch.long) 70 | 71 | for idx in range(batch_size): 72 | cur_len = word_seq_len[idx] # careful here, when curr_len<=max_allowed_len, that's ok 73 | if cur_len <= max_allowed_len: 74 | word_seq_tensor[idx, :word_seq_len[idx]] = torch.LongTensor(batch_data[idx].word_ids) 75 | if batch_data[idx].output_ids: 76 | label_seq_tensor[idx, :word_seq_len[idx]] = torch.LongTensor(batch_data[idx].output_ids) 77 | 78 | if batch_data[idx].is_prediction is not None: 79 | for pos in range(len(batch_data[idx].input)): 80 | if batch_data[idx].is_prediction[pos]: # if label is true (is about to be predicted) 81 | annotation_mask[idx, pos, :] = 1 82 | annotation_mask[idx, pos, config.start_label_id] = 0 83 | annotation_mask[idx, pos, config.stop_label_id] = 0 84 | else: 85 | annotation_mask[idx, pos, batch_data[idx].output_ids[pos]] = 1 # settled 86 | annotation_mask[idx, word_seq_len[idx]:, :] = 1 87 | else: 88 | word_seq_len[idx] = max_allowed_len 89 | # error 90 | word_seq_tensor[idx] = torch.LongTensor(batch_data[idx].word_ids[:max_allowed_len]) 91 | if batch_data[idx].output_ids: 92 | label_seq_tensor[idx] = torch.LongTensor(batch_data[idx].output_ids[:max_allowed_len]) 93 | 94 | if batch_data[idx].is_prediction is not None: 95 | for pos in range(len(batch_data[idx].input))[:max_allowed_len]: 96 | if batch_data[idx].is_prediction[pos]: # if label is true(is about to be predicted) 97 | annotation_mask[idx, pos, :] = 1 98 | annotation_mask[idx, pos, config.start_label_id] = 0 99 | annotation_mask[idx, pos, config.stop_label_id] = 0 100 | else: 101 | annotation_mask[idx, pos, batch_data[idx].output_ids[pos]] = 1 # settled 102 | annotation_mask[idx, word_seq_len[idx]:, :] = 1 103 | 104 | word_seq_tensor = word_seq_tensor.to(config.device) 105 | label_seq_tensor = label_seq_tensor.to(config.device) 106 | word_seq_len = word_seq_len.to(config.device) 107 | annotation_mask = annotation_mask.to(config.device) if annotation_mask is not None else None 108 | 109 | return word_seq_tensor, word_seq_len, annotation_mask, label_seq_tensor 110 | 111 | 112 | def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer: 113 | """ 114 | Method to decay the learning rate 115 | :param config: configuration 116 | :param optimizer: optimizer 117 | :param epoch: epoch number 118 | :return: 119 | """ 120 | lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1)) 121 | for param_group in optimizer.param_groups: 122 | param_group['lr'] = lr 123 | print('learning rate is set to: ', lr) 124 | return optimizer 125 | 126 | 127 | def get_optimizer(config: Config, model: nn.Module): 128 | params = model.parameters() 129 | if config.optimizer.lower() == "sgd": 130 | print( 131 | colored("Using SGD: lr is: {}, L2 regularization is: {}".format(config.learning_rate, config.l2), 'yellow')) 132 | return optim.SGD(params, lr=config.learning_rate, weight_decay=float(config.l2)) 133 | elif config.optimizer.lower() == "adam": 134 | print(colored("Using Adam", 'yellow')) 135 | return optim.Adam(params) 136 | else: 137 | print("Illegal optimizer: {}".format(config.optimizer)) 138 | exit(1) 139 | 140 | -------------------------------------------------------------------------------- /baseline/model/__init__.py: -------------------------------------------------------------------------------- 1 | from model.linear_crf_inferencer import LinearCRF 2 | -------------------------------------------------------------------------------- /baseline/model/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/bilstm_encoder.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/bilstm_encoder.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/charbilstm.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/charbilstm.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/linear_crf_inferencer.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/linear_crf_inferencer.cpython-36.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/linear_crf_inferencer.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/linear_crf_inferencer.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/model/__pycache__/neuralcrf.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/baseline/model/__pycache__/neuralcrf.cpython-37.pyc -------------------------------------------------------------------------------- /baseline/model/linear_crf_inferencer.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch 3 | 4 | from config import log_sum_exp_pytorch, START, STOP, PAD 5 | from typing import Tuple 6 | 7 | 8 | class LinearCRF(nn.Module): 9 | def __init__(self, config, print_info: bool = True): 10 | super(LinearCRF, self).__init__() 11 | 12 | self.label_size = config.label_size 13 | self.device = config.device 14 | 15 | self.label2idx = config.label2idx 16 | self.labels = config.idx2labels 17 | self.start_idx = self.label2idx[START] 18 | self.end_idx = self.label2idx[STOP] 19 | self.pad_idx = self.label2idx[PAD] 20 | 21 | # initialize the following transition (anything never -> start. end never -> anything. Same thing for the padding label) 22 | init_transition = torch.randn(self.label_size, self.label_size).to(self.device) 23 | init_transition[:, self.start_idx] = -10000.0 24 | init_transition[self.end_idx, :] = -10000.0 25 | init_transition[:, self.pad_idx] = -10000.0 26 | init_transition[self.pad_idx, :] = -10000.0 27 | 28 | self.transition = nn.Parameter(init_transition) 29 | 30 | def forward(self, lstm_scores, word_seq_lens, tags, mask): 31 | """ 32 | Calculate the negative log-likelihood 33 | :param lstm_scores: 34 | :param word_seq_lens: 35 | :param tags: 36 | :param mask: 37 | :return: 38 | """ 39 | all_scores= self.calculate_all_scores(lstm_scores= lstm_scores) 40 | unlabed_score = self.forward_unlabeled(all_scores, word_seq_lens) 41 | labeled_score = self.forward_labeled(all_scores, word_seq_lens, tags, mask) 42 | return unlabed_score, labeled_score 43 | 44 | def forward_unlabeled(self, all_scores: torch.Tensor, word_seq_lens: torch.Tensor) -> torch.Tensor: 45 | """ 46 | Calculate the scores with the forward algorithm. Basically calculating the normalization term 47 | :param all_scores: (batch_size x max_seq_len x num_labels x num_labels) from (lstm scores + transition scores). 48 | :param word_seq_lens: (batch_size) 49 | :return: (batch_size) for the normalization scores 50 | """ 51 | batch_size = all_scores.size(0) 52 | seq_len = all_scores.size(1) 53 | alpha = torch.zeros(batch_size, seq_len, self.label_size).to(self.device) 54 | 55 | alpha[:, 0, :] = all_scores[:, 0, self.start_idx, :] ## the first position of all labels = (the transition from start - > all labels) + current emission. 56 | 57 | for word_idx in range(1, seq_len): 58 | ## batch_size, self.label_size, self.label_size 59 | before_log_sum_exp = alpha[:, word_idx-1, :].view(batch_size, self.label_size, 1).expand(batch_size, self.label_size, self.label_size) + all_scores[:, word_idx, :, :] 60 | alpha[:, word_idx, :] = log_sum_exp_pytorch(before_log_sum_exp) 61 | 62 | ### batch_size x label_size 63 | last_alpha = torch.gather(alpha, 1, word_seq_lens.view(batch_size, 1, 1).expand(batch_size, 1, self.label_size)-1).view(batch_size, self.label_size) 64 | last_alpha += self.transition[:, self.end_idx].view(1, self.label_size).expand(batch_size, self.label_size) 65 | last_alpha = log_sum_exp_pytorch(last_alpha.view(batch_size, self.label_size, 1)).view(batch_size) 66 | 67 | return torch.sum(last_alpha) 68 | 69 | def forward_labeled(self, all_scores: torch.Tensor, word_seq_lens: torch.Tensor, tags: torch.Tensor, masks: torch.Tensor) -> torch.Tensor: 70 | ''' 71 | Calculate the scores for the gold instances. 72 | :param all_scores: (batch, seq_len, label_size, label_size) 73 | :param word_seq_lens: (batch, seq_len) 74 | :param tags: (batch, seq_len) 75 | :param masks: batch, seq_len 76 | :return: sum of score for the gold sequences Shape: (batch_size) 77 | ''' 78 | batchSize = all_scores.shape[0] 79 | sentLength = all_scores.shape[1] 80 | 81 | ## all the scores to current labels: batch, seq_len, all_from_label? 82 | currentTagScores = torch.gather(all_scores, 3, tags.view(batchSize, sentLength, 1, 1).expand(batchSize, sentLength, self.label_size, 1)).view(batchSize, -1, self.label_size) 83 | if sentLength != 1: 84 | tagTransScoresMiddle = torch.gather(currentTagScores[:, 1:, :], 2, tags[:, : sentLength - 1].view(batchSize, sentLength - 1, 1)).view(batchSize, -1) 85 | tagTransScoresBegin = currentTagScores[:, 0, self.start_idx] 86 | endTagIds = torch.gather(tags, 1, word_seq_lens.view(batchSize, 1) - 1) 87 | tagTransScoresEnd = torch.gather(self.transition[:, self.end_idx].view(1, self.label_size).expand(batchSize, self.label_size), 1, endTagIds).view(batchSize) 88 | score = torch.sum(tagTransScoresBegin) + torch.sum(tagTransScoresEnd) 89 | if sentLength != 1: 90 | score += torch.sum(tagTransScoresMiddle.masked_select(masks[:, 1:])) 91 | return score 92 | 93 | def calculate_all_scores(self, lstm_scores: torch.Tensor) -> torch.Tensor: 94 | """ 95 | Calculate all scores by adding up the transition scores and emissions (from lstm). 96 | Basically, compute the scores for each edges between labels at adjacent positions. 97 | This score is later be used for forward-backward inference 98 | :param lstm_scores: emission scores. 99 | :return: 100 | """ 101 | batch_size = lstm_scores.size(0) 102 | seq_len = lstm_scores.size(1) 103 | scores = self.transition.view(1, 1, self.label_size, self.label_size).expand(batch_size, seq_len, self.label_size, self.label_size) + \ 104 | lstm_scores.view(batch_size, seq_len, 1, self.label_size).expand(batch_size, seq_len, self.label_size, self.label_size) 105 | return scores 106 | 107 | def decode(self, features, wordSeqLengths, annotation_mask = None) -> Tuple[torch.Tensor, torch.Tensor]: 108 | """ 109 | Decode the batch input 110 | :param batchInput: 111 | :return: 112 | """ 113 | all_scores = self.calculate_all_scores(features) 114 | bestScores, decodeIdx = self.constrainted_viterbi_decode(all_scores, wordSeqLengths, annotation_mask) 115 | return bestScores, decodeIdx 116 | 117 | def constrainted_viterbi_decode(self, all_scores: torch.Tensor, word_seq_lens: torch.Tensor, annotation_mask: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor]: 118 | """ 119 | Use viterbi to decode the instances given the scores and transition parameters 120 | :param all_scores: (batch_size x max_seq_len x num_labels) 121 | :param word_seq_lens: (batch_size) 122 | :return: the best scores as well as the predicted label ids. 123 | (batch_size) and (batch_size x max_seq_len) 124 | """ 125 | batchSize = all_scores.shape[0] 126 | sentLength = all_scores.shape[1] 127 | if annotation_mask is not None: 128 | annotation_mask = annotation_mask.float().log() 129 | # sent_len = 130 | scoresRecord = torch.zeros([batchSize, sentLength, self.label_size]).to(self.device) 131 | idxRecord = torch.zeros([batchSize, sentLength, self.label_size], dtype=torch.int64).to(self.device) 132 | mask = torch.ones_like(word_seq_lens, dtype=torch.int64).to(self.device) 133 | startIds = torch.full((batchSize, self.label_size), self.start_idx, dtype=torch.int64).to(self.device) 134 | decodeIdx = torch.LongTensor(batchSize, sentLength).to(self.device) 135 | 136 | scores = all_scores 137 | # scoresRecord[:, 0, :] = self.getInitAlphaWithBatchSize(batchSize).view(batchSize, self.label_size) 138 | scoresRecord[:, 0, :] = scores[:, 0, self.start_idx, :] # represent the best current score from the start, is the best 139 | if annotation_mask is not None: 140 | scoresRecord[:, 0, :] += annotation_mask[:, 0, :] 141 | idxRecord[:, 0, :] = startIds 142 | for wordIdx in range(1, sentLength): 143 | # scoresIdx: batch x from_label x to_label at current index. 144 | scoresIdx = scoresRecord[:, wordIdx - 1, :].view(batchSize, self.label_size, 1).expand(batchSize, self.label_size, 145 | self.label_size) + scores[:, wordIdx, :, :] 146 | if annotation_mask is not None: 147 | scoresIdx += annotation_mask[:, wordIdx, :].view(batchSize, 1, self.label_size).expand(batchSize, self.label_size, self.label_size) 148 | 149 | idxRecord[:, wordIdx, :] = torch.argmax(scoresIdx, 1) # the best previous label idx to crrent labels 150 | scoresRecord[:, wordIdx, :] = torch.gather(scoresIdx, 1, idxRecord[:, wordIdx, :].view(batchSize, 1, self.label_size)).view(batchSize, self.label_size) 151 | 152 | lastScores = torch.gather(scoresRecord, 1, word_seq_lens.view(batchSize, 1, 1).expand(batchSize, 1, self.label_size) - 1).view(batchSize, self.label_size) ##select position 153 | lastScores += self.transition[:, self.end_idx].view(1, self.label_size).expand(batchSize, self.label_size) 154 | decodeIdx[:, 0] = torch.argmax(lastScores, 1) 155 | bestScores = torch.gather(lastScores, 1, decodeIdx[:, 0].view(batchSize, 1)) 156 | 157 | for distance2Last in range(sentLength - 1): 158 | lastNIdxRecord = torch.gather(idxRecord, 1, torch.where(word_seq_lens - distance2Last - 1 > 0, word_seq_lens - distance2Last - 1, mask).view(batchSize, 1, 1).expand(batchSize, 1, self.label_size)).view(batchSize, self.label_size) 159 | decodeIdx[:, distance2Last + 1] = torch.gather(lastNIdxRecord, 1, decodeIdx[:, distance2Last].view(batchSize, 1)).view(batchSize) 160 | 161 | return bestScores, decodeIdx -------------------------------------------------------------------------------- /baseline/run.sh: -------------------------------------------------------------------------------- 1 | python3 train_model.py --dataset data --num_epochs 30 --model_folder saved_model --seed 2020 --device_num 0 -------------------------------------------------------------------------------- /baseline/train_model.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import random 3 | import numpy as np 4 | from typing import Tuple 5 | from config import Reader, Config, evaluate_batch_insts, batching_list_instances 6 | import time 7 | import torch 8 | from typing import List 9 | from common import Instance 10 | import os 11 | import logging 12 | import pickle 13 | import math 14 | import itertools 15 | from torch.optim import Adam 16 | import torch.nn as nn 17 | from torch.optim.lr_scheduler import LambdaLR 18 | from transformers import BertConfig 19 | from bert_model import BertCRF 20 | import utils 21 | 22 | os.environ['CUDA_VISIBLE_DEVICES'] = '0' 23 | 24 | 25 | def set_seed(opt, seed): 26 | random.seed(seed) 27 | np.random.seed(seed) 28 | torch.manual_seed(seed) 29 | if opt.device.startswith("cuda"): 30 | print("using GPU...", torch.cuda.current_device()) 31 | torch.cuda.manual_seed(seed) 32 | torch.cuda.manual_seed_all(seed) 33 | 34 | 35 | def parse_arguments_t(parser): 36 | # Training Hyperparameters 37 | parser.add_argument('--device', type=str, default="cuda", choices=['cpu', 'cuda'], 38 | help="GPU/CPU devices") 39 | parser.add_argument('--seed', type=int, default=2019, help="random seed") 40 | parser.add_argument('--digit2zero', action="store_true", default=False, 41 | help="convert the number to 0, make it true is better") 42 | parser.add_argument('--dataset', type=str, default="data") 43 | parser.add_argument('--optimizer', type=str, default="sgd") 44 | parser.add_argument('--learning_rate', type=float, default=1e-4) 45 | parser.add_argument('--momentum', type=float, default=0.0) 46 | parser.add_argument('--l2', type=float, default=1e-8) 47 | parser.add_argument('--lr_decay', type=float, default=0) 48 | parser.add_argument('--batch_size', type=int, default=32, help="default batch size is 32 (works well)") 49 | parser.add_argument('--num_epochs', type=int, default=30, help="Usually we set to 10.") # origin 100 50 | parser.add_argument('--train_num', type=int, default=-1, help="-1 means all the data") 51 | parser.add_argument('--dev_num', type=int, default=-1, help="-1 means all the data") 52 | parser.add_argument('--num_outer_iterations', type=int, default=10, help="Number of outer iterations for cross validation") 53 | 54 | # bert hyperparameter 55 | parser.add_argument('--bert_model_dir', default='bert-base-chinese-pytorch', help="Directory containing the BERT model in PyTorch") 56 | parser.add_argument('--max_len', default=180, help="max allowed sequence length") 57 | parser.add_argument('--full_finetuning', default=True, action='store_true', 58 | help="Whether to fine tune the pre-trained model") 59 | parser.add_argument('--clip_grad', default=5, help="gradient clipping") 60 | 61 | # model hyperparameter 62 | parser.add_argument('--model_folder', type=str, default="saved_model", help="The name to save the model files") 63 | parser.add_argument('--device_num', type=str, default='0', help="The gpu number you want to use") 64 | 65 | args = parser.parse_args() 66 | for k in args.__dict__: 67 | print(k + ": " + str(args.__dict__[k])) 68 | return args 69 | 70 | 71 | def train_model(config: Config, train_insts: List[List[Instance]], dev_insts: List[Instance]): 72 | train_num = sum([len(insts) for insts in train_insts]) 73 | logging.info(("[Training Info] number of instances: %d" % (train_num))) 74 | # get the batched data 75 | dev_batches = batching_list_instances(config, dev_insts) 76 | 77 | model_folder = config.model_folder 78 | 79 | logging.info("[Training Info] The model will be saved to: %s" % (model_folder)) 80 | if not os.path.exists(model_folder): 81 | os.makedirs(model_folder) 82 | 83 | num_outer_iterations = config.num_outer_iterations 84 | 85 | for iter in range(num_outer_iterations): 86 | 87 | logging.info(f"[Training Info] Running for {iter}th large iterations.") 88 | 89 | model_names = [] # model names for each fold 90 | 91 | train_batches = [batching_list_instances(config, insts) for insts in train_insts] 92 | 93 | logging.info("length of train_insts：%d"% len(train_insts)) 94 | 95 | # train 2 models in 2 folds 96 | for fold_id, folded_train_insts in enumerate(train_insts): 97 | logging.info(f"[Training Info] Training fold {fold_id}.") 98 | # Initialize bert model 99 | logging.info("Initialized from pre-trained Model") 100 | 101 | model_name = model_folder + f"/bert_crf_{fold_id}" 102 | model_names.append(model_name) 103 | train_one(config=config, train_batches=train_batches[fold_id], 104 | dev_insts=dev_insts, dev_batches=dev_batches, model_name=model_name) 105 | 106 | # assign prediction to other folds 107 | logging.info("\n\n") 108 | logging.info("[Data Info] Assigning labels") 109 | 110 | # using the model trained in one fold to predict the result of another fold's data 111 | # and update the label of another fold with the predict result 112 | for fold_id, folded_train_insts in enumerate(train_insts): 113 | 114 | cfig_path = os.path.join(config.bert_model_dir, 'bert_config.json') 115 | cfig = BertConfig.from_json_file(cfig_path) 116 | cfig.device = config.device 117 | cfig.label2idx = config.label2idx 118 | cfig.label_size = config.label_size 119 | cfig.idx2labels = config.idx2labels 120 | 121 | model_name = model_folder + f"/bert_crf_{fold_id}" 122 | model = BertCRF(cfig=cfig) 123 | model.to(cfig.device) 124 | utils.load_checkpoint(os.path.join(model_name, 'best.pth.tar'), model) 125 | 126 | hard_constraint_predict(config=config, model=model, 127 | fold_batches=train_batches[1 - fold_id], 128 | folded_insts=train_insts[1 - fold_id]) # set a new label id, k is set to 2, so 1 - fold_id can be used 129 | logging.info("\n\n") 130 | 131 | logging.info("[Training Info] Training the final model") 132 | 133 | # merge the result data to training the final model 134 | all_train_insts = list(itertools.chain.from_iterable(train_insts)) 135 | 136 | logging.info("Initialized from pre-trained Model") 137 | 138 | model_name = model_folder + "/final_bert_crf" 139 | config_name = model_folder + "/config.conf" 140 | 141 | all_train_batches = batching_list_instances(config=config, insts=all_train_insts) 142 | # train the final model 143 | model = train_one(config=config, train_batches=all_train_batches, dev_insts=dev_insts, dev_batches=dev_batches, 144 | model_name=model_name, config_name=config_name) 145 | # load the best final model 146 | utils.load_checkpoint(os.path.join(model_name, 'best.pth.tar'), model) 147 | model.eval() 148 | logging.info("\n") 149 | result = evaluate_model(config, model, dev_batches, "dev", dev_insts) 150 | logging.info("\n\n") 151 | 152 | 153 | def hard_constraint_predict(config: Config, model: BertCRF, fold_batches: List[Tuple], folded_insts:List[Instance], model_type:str = "hard"): 154 | """using the model trained in one fold to predict the result of another fold""" 155 | batch_id = 0 156 | batch_size = config.batch_size 157 | model.eval() 158 | for batch in fold_batches: 159 | one_batch_insts = folded_insts[batch_id * batch_size:(batch_id + 1) * batch_size] 160 | 161 | input_ids, input_seq_lens, annotation_mask, labels = batch 162 | input_masks = input_ids.gt(0) 163 | # get the predict result 164 | batch_max_scores, batch_max_ids = model(input_ids, input_seq_lens=input_seq_lens, 165 | annotation_mask=annotation_mask, labels=None, attention_mask=input_masks) 166 | 167 | batch_max_ids = batch_max_ids.cpu().numpy() 168 | word_seq_lens = batch[1].cpu().numpy() 169 | for idx in range(len(batch_max_ids)): 170 | length = word_seq_lens[idx] 171 | prediction = batch_max_ids[idx][:length].tolist() 172 | prediction = prediction[::-1] 173 | # update the labels of another fold 174 | one_batch_insts[idx].output_ids = prediction 175 | batch_id += 1 176 | 177 | 178 | def train_one(config: Config, train_batches: List[Tuple], dev_insts: List[Instance], dev_batches: List[Tuple], 179 | model_name: str, config_name: str = None) -> BertCRF: 180 | 181 | # load config for bertCRF 182 | cfig_path = os.path.join(config.bert_model_dir, 183 | 'bert_config.json') 184 | cfig = BertConfig.from_json_file(cfig_path) 185 | cfig.device = config.device 186 | cfig.label2idx = config.label2idx 187 | cfig.label_size = config.label_size 188 | cfig.idx2labels = config.idx2labels 189 | # load pretrained bert model 190 | model = BertCRF.from_pretrained(config.bert_model_dir, config=cfig) 191 | model.to(config.device) 192 | 193 | if config.full_finetuning: 194 | logging.info('full finetuning') 195 | param_optimizer = list(model.named_parameters()) 196 | no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] 197 | optimizer_grouped_parameters = [ 198 | {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 199 | 'weight_decay_rate': 0.01}, 200 | {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 201 | 'weight_decay_rate': 0.0} 202 | ] 203 | 204 | else: 205 | logging.info('tuning downstream layer') 206 | param_optimizer = list(model.classifier.named_parameters()) 207 | optimizer_grouped_parameters = [{'params': [p for n, p in param_optimizer]}] 208 | 209 | optimizer = Adam(optimizer_grouped_parameters, lr=config.learning_rate) 210 | scheduler = LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)) 211 | 212 | model.train() 213 | 214 | epoch = config.num_epochs 215 | best_dev_f1 = -1 216 | for i in range(1, epoch + 1): 217 | epoch_loss = 0 218 | start_time = time.time() 219 | model.zero_grad() 220 | 221 | for index in np.random.permutation(len(train_batches)): # disorder the train batches 222 | model.train() 223 | scheduler.step() 224 | input_ids, input_seq_lens, annotation_mask, labels = train_batches[index] 225 | input_masks = input_ids.gt(0) 226 | # update loss 227 | loss = model(input_ids, input_seq_lens=input_seq_lens, annotation_mask=annotation_mask, 228 | labels=labels, attention_mask=input_masks) 229 | epoch_loss += loss.item() 230 | model.zero_grad() 231 | loss.backward() 232 | # gradient clipping 233 | nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=config.clip_grad) 234 | optimizer.step() 235 | end_time = time.time() 236 | logging.info("Epoch %d: %.5f, Time is %.2fs" % (i, epoch_loss, end_time - start_time)) 237 | 238 | model.eval() 239 | with torch.no_grad(): 240 | # metric is [precision, recall, f_score] 241 | dev_metrics = evaluate_model(config, model, dev_batches, "dev", dev_insts) 242 | if dev_metrics[2] > best_dev_f1: # save the best model 243 | logging.info("saving the best model...") 244 | best_dev_f1 = dev_metrics[2] 245 | 246 | model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self 247 | optimizer_to_save = optimizer 248 | utils.save_checkpoint({'epoch': epoch + 1, 249 | 'state_dict': model_to_save.state_dict(), 250 | 'optim_dict': optimizer_to_save.state_dict()}, 251 | is_best=dev_metrics[2] > 0, 252 | checkpoint=model_name) 253 | 254 | # Save the corresponding config as well. 255 | if config_name: 256 | f = open(config_name, 'wb') 257 | pickle.dump(config, f) 258 | f.close() 259 | model.zero_grad() 260 | 261 | return model 262 | 263 | 264 | def evaluate_model(config: Config, model: BertCRF, batch_insts_ids, name: str, insts: List[Instance]): 265 | # evaluation 266 | metrics = np.asarray([0, 0, 0], dtype=int) 267 | batch_id = 0 268 | batch_size = config.batch_size 269 | for batch in batch_insts_ids: 270 | one_batch_insts = insts[batch_id * batch_size:(batch_id + 1) * batch_size] 271 | 272 | input_ids, input_seq_lens, annotation_mask, labels = batch 273 | input_masks = input_ids.gt(0) 274 | # get the predict result 275 | batch_max_scores, batch_max_ids = model(input_ids, input_seq_lens=input_seq_lens, annotation_mask=annotation_mask, 276 | labels=None, attention_mask=input_masks) 277 | 278 | metrics += evaluate_batch_insts(batch_insts=one_batch_insts, 279 | batch_pred_ids=batch_max_ids, 280 | batch_gold_ids=batch[-1], 281 | word_seq_lens=batch[1], idx2label=config.idx2labels) 282 | batch_id += 1 283 | # calculate the precision, recall and f1 score 284 | p, total_predict, total_entity = metrics[0], metrics[1], metrics[2] 285 | precision = p * 1.0 / total_predict * 100 if total_predict != 0 else 0 286 | recall = p * 1.0 / total_entity * 100 if total_entity != 0 else 0 287 | fscore = 2.0 * precision * recall / (precision + recall) if precision != 0 or recall != 0 else 0 288 | logging.info("[%s set] Precision: %.2f, Recall: %.2f, F1: %.2f" % (name, precision, recall, fscore)) 289 | return [precision, recall, fscore] 290 | 291 | 292 | def main(): 293 | logging.info("Transformer implementation") 294 | parser = argparse.ArgumentParser(description="Transformer CRF implementation") 295 | opt = parse_arguments_t(parser) 296 | conf = Config(opt) 297 | conf.train_file = conf.dataset + "/train.txt" 298 | conf.dev_file = conf.dataset + "/valid.txt" 299 | os.environ['CUDA_VISIBLE_DEVICES'] = opt.device_num 300 | # data reader 301 | reader = Reader(conf.digit2zero) 302 | set_seed(opt, conf.seed) 303 | 304 | # set logger 305 | utils.set_logger(os.path.join(conf.model_folder, 'train.log')) 306 | 307 | # params 308 | for k in opt.__dict__: 309 | logging.info(k + ": " + str(opt.__dict__[k])) 310 | 311 | # read trains/devs 312 | logging.info("\n") 313 | logging.info("Loading the datasets...") 314 | trains = reader.read_txt(conf.train_file, conf.train_num) 315 | devs = reader.read_txt(conf.dev_file, conf.dev_num) 316 | 317 | logging.info("Building label idx ...") 318 | # build label2idx and idx2label 319 | conf.build_label_idx(trains + devs) 320 | 321 | random.shuffle(trains) 322 | # set the prediction flag, if is_prediction is False, we will not update this label. 323 | for inst in trains: 324 | inst.is_prediction = [False] * len(inst.input) 325 | for pos, label in enumerate(inst.output): 326 | if label == conf.O: 327 | inst.is_prediction[pos] = True 328 | # dividing the data into 2 parts(num_folds default to 2) 329 | num_insts_in_fold = math.ceil(len(trains) / conf.num_folds) 330 | trains = [trains[i * num_insts_in_fold: (i + 1) * num_insts_in_fold] for i in range(conf.num_folds)] 331 | 332 | train_model(config=conf, train_insts=trains, dev_insts=devs) 333 | 334 | 335 | if __name__ == "__main__": 336 | main() 337 | -------------------------------------------------------------------------------- /baseline/utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | import logging 3 | import os 4 | import shutil 5 | 6 | import torch 7 | 8 | 9 | class Params(): 10 | """Class that loads hyperparameters from a json file. 11 | 12 | Example: 13 | ``` 14 | params = Params(json_path) 15 | print(params.learning_rate) 16 | params.learning_rate = 0.5 # change the value of learning_rate in params 17 | ``` 18 | """ 19 | 20 | def __init__(self, json_path): 21 | with open(json_path) as f: 22 | params = json.load(f) 23 | self.__dict__.update(params) 24 | 25 | def save(self, json_path): 26 | with open(json_path, 'w') as f: 27 | json.dump(self.__dict__, f, indent=4) 28 | 29 | def update(self, json_path): 30 | """Loads parameters from json file""" 31 | with open(json_path) as f: 32 | params = json.load(f) 33 | self.__dict__.update(params) 34 | 35 | @property 36 | def dict(self): 37 | """Gives dict-like access to Params instance by `params.dict['learning_rate']""" 38 | return self.__dict__ 39 | 40 | 41 | class RunningAverage(): 42 | """A simple class that maintains the running average of a quantity 43 | 44 | Example: 45 | ``` 46 | loss_avg = RunningAverage() 47 | loss_avg.update(2) 48 | loss_avg.update(4) 49 | loss_avg() = 3 50 | ``` 51 | """ 52 | 53 | def __init__(self): 54 | self.steps = 0 55 | self.total = 0 56 | 57 | def update(self, val): 58 | self.total += val 59 | self.steps += 1 60 | 61 | def __call__(self): 62 | return self.total / float(self.steps) 63 | 64 | 65 | def set_logger(log_path): 66 | """Set the logger to log info in terminal and file `log_path`. 67 | 68 | In general, it is useful to have a logger so that every output to the terminal is saved 69 | in a permanent file. Here we save it to `model_dir/train.log`. 70 | 71 | Example: 72 | ``` 73 | logging.info("Starting training...") 74 | ``` 75 | 76 | Args: 77 | log_path: (string) where to log 78 | """ 79 | logger = logging.getLogger() 80 | logger.setLevel(logging.INFO) 81 | 82 | if logger.handlers: # revised 83 | logger.handlers.clear() 84 | print('logging file and console support') 85 | # Logging to a file 86 | file_handler = logging.FileHandler(log_path) 87 | file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s')) 88 | logger.addHandler(file_handler) 89 | 90 | # Logging to console 91 | stream_handler = logging.StreamHandler() 92 | stream_handler.setFormatter(logging.Formatter('%(message)s')) 93 | logger.addHandler(stream_handler) 94 | 95 | 96 | def save_checkpoint(state, is_best, checkpoint): 97 | """Saves model and training parameters at checkpoint + 'last.pth.tar'. If is_best==True, also saves 98 | checkpoint + 'best.pth.tar' 99 | 100 | Args: 101 | state: (dict) contains model's state_dict, may contain other keys such as epoch, optimizer state_dict 102 | is_best: (bool) True if it is the best model seen till now 103 | checkpoint: (string) folder where parameters are to be saved 104 | """ 105 | filepath = os.path.join(checkpoint, 'last.pth.tar') 106 | if not os.path.exists(checkpoint): 107 | print("Checkpoint Directory does not exist! Making directory {}".format(checkpoint)) 108 | os.mkdir(checkpoint) 109 | torch.save(state, filepath) 110 | if is_best: 111 | shutil.copyfile(filepath, os.path.join(checkpoint, 'best.pth.tar')) 112 | 113 | 114 | def load_checkpoint(checkpoint, model, optimizer=None): 115 | """Loads model parameters (state_dict) from file_path. If optimizer is provided, loads state_dict of 116 | optimizer assuming it is present in checkpoint. 117 | 118 | Args: 119 | checkpoint: (string) filename which needs to be loaded 120 | model: (torch.nn.Module) model for which the parameters are loaded 121 | optimizer: (torch.optim) optional: resume optimizer from checkpoint 122 | """ 123 | if not os.path.exists(checkpoint): 124 | raise ("File doesn't exist {}".format(checkpoint)) 125 | checkpoint = torch.load(checkpoint) 126 | model.load_state_dict(checkpoint['state_dict']) 127 | 128 | if optimizer: 129 | optimizer.load_state_dict(checkpoint['optim_dict']) 130 | 131 | return checkpoint 132 | 133 | -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/data/.DS_Store -------------------------------------------------------------------------------- /data/train_dict/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZhuiyiTechnology/AutoIE/321ec39afa3c65c1bf6fbc2c74117f8b20d35ded/data/train_dict/.DS_Store -------------------------------------------------------------------------------- /data/train_dict/NUM.txt: -------------------------------------------------------------------------------- 1 | 第四十二季 2 | 第一季 3 | 第1部 4 | 第1季 5 | 第一部 6 | 第六季 7 | 1部 8 | 第五季 9 | 第7季 10 | 第二十三季 11 | 第四季 12 | 第一季 13 | 第10季 14 | 第四季 15 | 第3部 16 | 第6季 17 | 第十季 18 | 第二幕 19 | 第2期 20 | 第八季 21 | 第4套 22 | 第三套 -------------------------------------------------------------------------------- /data/train_dict/PER.txt: -------------------------------------------------------------------------------- 1 | 李湘 2 | 岳云鹏 3 | 陈妍希 4 | 鹿晗 5 | 谢霆锋 6 | 马馺 7 | 胡歌 8 | 成龙 9 | 小刚 10 | 刘昊然 11 | 于谦 12 | 张卫健 13 | 李安然 14 | 旋风 15 | 李娜 16 | 陈柳 17 | 黄磊 18 | 周柏豪 19 | 陈奕迅 20 | 瑟妃 21 | 周星驰 22 | 马德钟 23 | 黄渤 24 | 郑雨盛 25 | 宫崎骏 26 | 李雪健 27 | 影魔 28 | 杨千嬅 29 | 凌波玲 30 | 叁金壹良 31 | 廉廉 32 | 陈浩民 33 | 小书 34 | 刘涛 35 | 鹿晗 36 | Clown 37 | 罗志祥 38 | 施瓦辛格 39 | 霍建华 40 | 沈凯晨 41 | 马可 42 | Paramore 43 | 沈腾 44 | 陈伟霆 45 | 程荣杰 46 | 朱一龙 47 | 孙俪 48 | 赵权 49 | 诺一 50 | 糖宝 51 | 小骨 52 | 小沈阳 53 | 籽岷 54 | 归魂百战 55 | 秋瓷炫 56 | 王重阳 57 | 王祖蓝 58 | 林志玲 59 | 马修鹏 60 | 任达华 61 | 吴秀波 62 | 朱长安 63 | 杨颖 64 | 郑爽 65 | 李小鹏 66 | TFBOYS 67 | 佘诗曼 68 | 欧阳娜娜 69 | 苏有朋 70 | 梁朝伟 71 | coco星痕 72 | 孙越 73 | 九墨 74 | JJCC 75 | 汤唯 76 | Baby 77 | 林永健 78 | 旺堆 79 | 释小龙 80 | 小不点 81 | 小彬 82 | 吴倩莲 83 | 郭富城 84 | 神降 85 | 罗晋 86 | 黄日华 87 | 尹光 88 | 刘伯温 89 | 晨逸 90 | 周冬雨 91 | 鑫魂 92 | 董洁 93 | 火星哥 94 | 赵雅芝 95 | 叶童 96 | 陈美琪 97 | 托马斯·米德迪 98 | 吴谨言 99 | 刘丽 100 | 方中信 101 | 籽墨 102 | 良民 103 | 范冰冰 104 | 娜扎 105 | 逆风笑 106 | 谢涤葵 107 | 张嘉译 108 | 韦礼安 109 | 金秀贤 110 | 王力宏 111 | 白仔 112 | 孙超 113 | 米兹 114 | 刘思彤 115 | 高峰 116 | 李文均 117 | 金晨 118 | 郭美美 119 | 井柏然 120 | 吴镇宇 121 | 赵丽蓉 122 | 冉阿让 123 | 吴莫愁 124 | 凃磊 125 | 邓丽欣 126 | 毒蛇 127 | 乾隆 128 | 李钟硕 129 | 郑仲亮 130 | 胡海泉 131 | 罗友非 132 | 快乐家族 133 | 袁姗姗 134 | 小跃 135 | 海涛 136 | 林宇轩 137 | SNH48 138 | 蓝若恬 139 | 文松 140 | 蒋伍轩 141 | 吴刚 142 | 苹果 143 | 丁大命 144 | 林黛 145 | 凌冬 146 | 刘俊海 147 | 罗素克劳 148 | 小爱 149 | 刘烨 150 | 周妈妈 151 | 林冲 152 | 掸掸 153 | A家FG王畅 154 | 姜文 155 | 胡喆 156 | 马云 157 | 乔欣 158 | 囧脸 159 | 范文芳 160 | 浅茶 161 | 于智博 162 | 鲁豫 163 | 麦积柒少 164 | 易烊千玺 165 | 周秀娜 166 | 郑少秋 167 | 羽泉 168 | 吴大维 169 | esther 170 | 仲王 171 | 杨飞 172 | 丹尼斯吴 173 | 蒋梦婕 174 | 丫头 175 | 魔术杨 176 | 萌男 177 | 陈世美 178 | 白凯南 179 | simplyunlucky 180 | 吴语 181 | 乔恩 182 | 朴娜娜 183 | Bigbang 184 | 赵传 185 | 薛之谦 186 | 颖儿 187 | 傅晶 188 | nada 189 | 李乃文 190 | 小酥松 191 | pink 192 | 王昆 193 | 陈都灵 194 | 关晓彤 195 | 马思纯 196 | 幽麟 197 | 迈克尔杰克逊 198 | 一炫 199 | 小致 200 | 高桥南 201 | 狄龙 202 | 邓伦 203 | 杉原杏璃 204 | 李克勤 205 | 小灿 206 | 陈松伶 207 | 马列 208 | 徐冬冬 209 | 雍正 210 | 具惠善 211 | 李民浩 212 | 金范 213 | 风小筝 214 | 杨碧玉 215 | 刘三妹 216 | 张铭恩 217 | 赵宇航 218 | 张云帆 219 | 贤哥 220 | infinite阿囧 221 | 聂远 222 | 莫妮卡·贝鲁 223 | 吴武强 224 | 余文乐 225 | 凯丽 226 | 斯琴高娃 227 | 梁植深 228 | 费曼 229 | 新之助之新 230 | 轻风 231 | 小然 232 | 巩汉林 233 | 孙芮 234 | 沙溢 235 | 羽霏 236 | 尹子维 237 | 谭咏麟 238 | 雪梨 239 | 慈禧 240 | 粉鱼 241 | 布鲁斯 242 | 小白 243 | 刘畅 244 | 林武樟 245 | 李菁 246 | JSXmas 247 | 刘真 248 | 阿里 249 | 钟淑 250 | 王学圻 251 | 刘恺 252 | 林书豪 253 | 米奇 254 | 包子哥 255 | 华仔 256 | 刘镇伟 257 | 瓜子 258 | 马天宇 259 | 刘蓓 260 | 文章 261 | 何立清 262 | 炎亚纶 263 | 徐长卿 264 | 热狗 265 | 趙麗穎 266 | 抽烟 267 | 谷峰 268 | 狂湿 269 | 孙悦 270 | 谭俊彦 271 | 朱晨丽 272 | 孟玄朗 273 | LuHan 274 | 飞翔鸟乐队 275 | 黄菡 276 | 星爷 277 | 鑫晶 278 | 朴恩惠 279 | 凤姐 280 | 芙蓉姐姐 281 | 古龙 282 | 李亚鹏 283 | 于于 284 | 陈意涵 285 | 大飞-黄 286 | 涂磊 287 | 周迅 288 | 倪子钧 289 | 小欢哥 290 | 谭卓 291 | 李维嘉 292 | 徐佳莹 293 | 庾澄庆 294 | Andy哥 295 | 周笔畅 296 | 花正 297 | 糖醋果果 298 | 袁弘 299 | 曹格 300 | 李安妮 301 | 菲儿 302 | 雨轩 303 | 李丽华 304 | 荣蓉 305 | 轩轩 306 | 张家辉 307 | 姚笛 308 | 梁贤 309 | 马伊琍 310 | 李国豪 311 | 陈莎莎 312 | 郭涛 313 | 粱家辉 314 | 李瑞镇 315 | 玉泽演 316 | 金智英 317 | 孙泰英 318 | 郑丽媛 319 | 小天哥 320 | 五月天 321 | 泰森 322 | 專屬の凡寳 323 | 南阳任国熙 324 | 高亚麟 325 | 王健林 326 | 黛米摩尔 327 | 湘君 328 | 应采儿 329 | 胡杏儿 330 | 杨蓉 331 | 王铮 332 | 籽辰 333 | 刘宇光 334 | 文东俊 335 | 蔡明 336 | 英壮 337 | 德哥 338 | 猪猪 339 | 冯远征 340 | 严基俊 341 | 威廉-莎士比亚 342 | 郎鹤炎 343 | 吴彦祖 344 | 曹丕 345 | 刘楷俊 346 | 乔布斯 347 | 薛佳凝 348 | Cindy 349 | 童蕾 350 | 贝儿 351 | 飞轮海 352 | 李老师 353 | 无味蓝染 354 | 钟镇涛 355 | 柳大华 356 | 王冠雄 357 | 金多喜 358 | 姚晨 359 | 秀罗先端 360 | 张娜拉 361 | 张檬 362 | 苍穹 363 | 梁凯恩 364 | 张子萱 365 | 孙怡 366 | 金钟国 367 | 阿铁 368 | 嫣儿 369 | 梁静茹 370 | 常洪涛 371 | 孙恺妍 372 | 李茂 373 | 蓝盈莹 374 | 栾云平 375 | 马雅舒 376 | 昌珉 377 | 官晶晶 378 | 朱刚日尧 379 | 小满 380 | 利刃孤锋 381 | 赫本 382 | zi_yai 383 | 魏晨 384 | 玄彬 385 | 宋慧乔 386 | 李成龙 387 | 李沁 388 | 小煋 389 | 金宇轩 390 | 张学良 391 | 小伍 392 | 孙勇征 393 | 苗利明 394 | 王自健 395 | 明道 396 | 吴卓羲 397 | 杨怡 398 | 李倩 399 | 杨紫 400 | 林老师 401 | 张彬彬 402 | 王小利 403 | 孙丽荣 404 | 小贝 405 | 许允美 406 | 王妃 407 | 郑晓宁 408 | kaitlyn 409 | eve 410 | 蒋金阳 411 | 潇龙 412 | 江烽 413 | 赵炎 414 | 尹相杰 415 | 牟洋 416 | 洛辰 417 | 明月庄主 418 | 刘邦 419 | 雷恩斯痛 420 | 李秀彬 421 | 黄俊英 422 | 小虹虹 423 | 易中天 424 | 温碧霞 425 | 高玩 426 | 黄云 427 | 杰西卡 428 | 暮云 429 | 曼妮 430 | 老林 431 | 保剑锋 432 | 黄维德 433 | 东北F4 434 | 史敏 435 | 孙策 436 | 张翰 437 | 谢贤 438 | TOVE 439 | 托尼贾 440 | 刘丹 441 | 张岸 442 | 毛晓彤 443 | 大锤 444 | 孙中山 445 | 颜丹晨 446 | 邓辉鹏 447 | 湉湉 448 | 蛋黄 449 | 茜拉 450 | 风少 451 | 王泷 452 | 星痕 453 | 方力申 454 | tf家族 455 | 影佑 456 | 韩孝珠 457 | 李英雅 458 | 馨爷 459 | 豫畅 460 | 赵靖 461 | 泰歌 462 | 金刀先森 463 | 送葬者 464 | 子豪 465 | 抽风 466 | 刘国梁 467 | 刘凯威 468 | 安雅萍 469 | Biyang 470 | TuaSaranyu 471 | 孙一峰 472 | 刘维 473 | 梧桐 474 | 反町隆史 475 | teentop 476 | 李夏怡 477 | 东尼大木 478 | 郑秀晶 479 | 萧敬腾 480 | 白冰 481 | VIP小干 482 | 黄子华 483 | 马一凡 484 | 小黑 485 | 成都 486 | 张文顺 487 | 郭德纲 488 | 薛峰 489 | YOYO 490 | 钡钡 491 | 曹圆雪 492 | 袁咏仪 493 | 酷玩君 494 | 张静初 495 | 梅根 496 | 张云雷 497 | 阿姆瑞塔·拉奥 498 | 桂治洪 499 | 梁家辉 500 | 刘博文 501 | 乔杉 502 | 邱鸿彬 503 | 老张 504 | 史爱东 505 | 恶魔 506 | 夏夜 507 | 金来沅 508 | 威尔史密斯 509 | 海顿 510 | 斯温 511 | 李英爱 512 | 虚无 513 | 修罗c 514 | 张雪迎 515 | 杨达 516 | 小王 517 | 胡袁萍 518 | 王雅捷 519 | 弟弟 520 | 樊少皇 521 | 黄贯中 522 | 逍宏 523 | 阚清子 524 | 张丹峰 525 | 唐志中 526 | 李安国 527 | 于朦胧 528 | 威廉 529 | 娄艺潇 530 | 白鸽 531 | 新之助 532 | 芭比.BoB 533 | 曾沛慈 534 | SpeXial 535 | 寇仲 536 | 宋玉 537 | Biu小宇 538 | 王媛可 539 | 扁桃 540 | FORT 541 | 姚明 542 | 彦希 543 | SuperJunior 544 | Lunar 545 | 樊亦敏 546 | 周比利 547 | 朴有天 548 | 神风 549 | 妖妖 550 | 金善雅 551 | 王丽云 552 | 黑人 553 | 香取慎吾 554 | 陈百祥 555 | AKB48 556 | 天使 557 | 王传君 558 | 钱惠丽 559 | 神话Andy 560 | 小花妹妹 561 | 小大大 562 | 成哥 563 | 春风萧落 564 | wks 565 | 小米丸子 566 | 疯狂PVZ 567 | 吕良伟 568 | 全相彦 569 | 老湿 570 | 陈希伯 571 | 金显祐 572 | 咸者 573 | 陈安之 574 | 陈宝国 575 | 小涛 576 | 秦树强 577 | 金素恩 578 | 李顺载 579 | 凌云 580 | SHAHRUKH 581 | 邵兵 582 | LGD 583 | zjh鸿星尔克 584 | 张惠妹 585 | 姜武 586 | 征服者 587 | 小智 588 | 小莫 589 | 王小虎 590 | 刘松仁 591 | Yan 592 | Kobe 593 | 陶铸 594 | 周云峰 595 | 陈玉林 596 | 橙橙 597 | 于和伟 598 | 乔振宇 599 | 郭家铭 600 | 阿甘 601 | 妲己 602 | 玛丽莲梦露 603 | 余安安 604 | 潘虹 605 | 金瀚 606 | 林子闳 607 | 蔡国庆 608 | 单老师 609 | 秦俊杰 610 | 秀敏 611 | 武强 612 | 詹哥 613 | 王菊 614 | 李若彤 -------------------------------------------------------------------------------- /data/train_dict/TV.txt: -------------------------------------------------------------------------------- 1 | 蜡笔小新 2 | 奔跑吧兄弟 3 | 爸爸回来了 4 | 爸爸去哪儿 5 | runningman 6 | 新白娘子传奇 7 | 超凡蜘蛛侠 8 | 冲霄楼 9 | 终极三国 10 | 行尸走肉 11 | 祝你生日快乐 12 | 武媚娘传奇 13 | 风中奇缘 14 | 京华春梦 15 | 大话西游 16 | 夏目友人帐 17 | 晓说 18 | 燥公园 19 | 天堂里的异乡人 20 | 名侦探柯南 21 | 天龙八部 22 | 哈佛比耶鲁：29-29 23 | 虎口脱险 24 | 步步惊心 25 | 最强大脑 26 | 燃烧吧大脑 27 | 摘星梦难圆 28 | 假面骑士铠武 29 | 大帅哥 30 | 爱情保卫战 31 | 圆梦大师 32 | 刘家媳妇 33 | 速度与激情 34 | 胜者为王 35 | 小黄人大眼萌 36 | 十三密杀令 37 | 神经侠侣 38 | 裁判battle秀 39 | 武大的小姐姐们 40 | 荒野求生 41 | 赌圣 42 | 七龙珠 43 | 歌唱动荡的青春 44 | 延安颂 45 | 命转乾坤 46 | 篮球火 47 | 球爱酒吧 48 | 跑男 49 | 欢乐颂 50 | 安娜与武林 51 | 飞龙斩 52 | 梦想Dream 53 | 为你而来 54 | 小心“陷阱” 55 | 心灵干探 56 | 笑笑茶楼 57 | 动物世界 58 | GX 59 | 毛骗 60 | THELAST 61 | 秦时明月 62 | 舌尖上的中国 63 | 绝代双骄 64 | 凤凰血 65 | 熊出没 66 | 猪猪侠 67 | 粉红猪小妹 68 | 华人世界 69 | 你会怎么做 70 | 非常完美 71 | 蜗居 72 | 何以笙箫默 73 | 狐狸的夏天 74 | 月上昆仑 75 | 武陵山剿匪记 76 | 机密档案 77 | 孝庄秘史 78 | 借东西的小矮人-阿莉埃蒂 79 | 破坏者 80 | 地下铁 81 | 哆啦a梦 82 | 我们仨 83 | 独木桥 84 | 拯救希尔加 85 | 子婴魔魂 86 | 毛泽东与中国 87 | 蓝与黑 88 | 一周的偶像 89 | 呼风唤雨 90 | 欢乐喜剧人 91 | 欢喜就好之五星大饭店 92 | 刀剑神域 93 | 卷土重来 94 | 寻亲记 95 | 重耳传奇 96 | 完结篇 97 | 好久不见 98 | 我是僵尸 99 | 四平青年 100 | 翻开这一页 101 | 下一站幸福 102 | 主妇也要拼 103 | 中国命运的决战 104 | 我们的挑战 105 | 女人帮 106 | 穿透屋顶的highkick 107 | 娱乐有机物 108 | MEMORIESOFNOBODY 109 | 烽火边城 110 | 鹿鼎记 111 | 奴里 112 | ウルトラマンA 113 | 艾斯奥特曼 114 | 大唐双龙传 115 | 我要上春晚 116 | 带刀女捕快 117 | 警察故事 118 | 浪漫曲 119 | 易中天品三国 120 | 放羊的星星 121 | 高兴逮笨贼 122 | 复仇者联盟 123 | 纪录东方 124 | 恶客 125 | 音乐风云榜 126 | 少林寺传奇藏经阁 127 | 倩女幽魂 128 | 龙虎沟 129 | 风云1911 130 | 环球跨年冰雪盛典 131 | 康熙王朝 132 | 特警力量 133 | 仙侠奇缘 134 | 有一种电影叫香港 135 | 奥创时代 136 | 法证先锋 137 | 血洗红花亭 138 | yeehob夫人 139 | 恶爷 140 | 巴啦啦小魔仙 141 | 音符之谜 142 | 铁旗门 143 | 囧先生 144 | 千金归来 145 | 袁游 146 | 武林外传 147 | 特殊使命 148 | TidaPayayom 149 | 揭秘真相 150 | 这！就是原创 151 | 我爱满堂彩 152 | 中国正在听 153 | 杀无赦 154 | 我的梦幻婚纱:大日子(特别版) 155 | 新包青天 156 | 你是我的答案 157 | 大宋王朝 158 | 佐杜洛夫斯基的沙丘 159 | 亲爱的 160 | 上将洪学智 161 | 沉睡魔咒 162 | 都市巡警 163 | 快乐的小鸡 164 | 死神的十字路口 165 | 鬼4虐 166 | 最佳爱情 167 | 一年幽梦 168 | 食全食美 169 | 中国好人 170 | 重生归来 171 | 拳精 172 | O记实录 173 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"谢霆锋北京奥迪活动现场版十二道锋味CIMG1897_高清-端庄", "PER": [{"str": "谢霆锋", "start_position": 0, "end_position": 2}]} 921 | {"id": "00921", "query": "郭德纲于谦相声怯拜年_2", "PER": [{"str": "郭德纲", "start_position": 0, "end_position": 2}, {"str": "于谦", "start_position": 3, "end_position": 4}]} 922 | {"id": "00922", "query": "刘德华-独自去偷欢-2011Unforgettable(http://suo.im/iihqj)", "PER": [{"str": "刘德华", "start_position": 0, "end_position": 2}]} 923 | {"id": "00923", "query": "隐身君我的世界:《星球历险记》ep.1", "TV": [{"str": "隐身君我", "start_position": 0, "end_position": 3}, {"str": "星球历险记", "start_position": 9, "end_position": 13}]} 924 | {"id": "00924", "query": "熊出没之夺宝熊兵片段1光头强的箱子", "TV": [{"str": "熊出没", "start_position": 0, "end_position": 2}, {"str": "夺宝熊兵", "start_position": 4, "end_position": 7}]} 925 | {"id": "00925", "query": "武媚娘传奇46_高清", "TV": [{"str": "武媚娘传奇", "start_position": 0, "end_position": 4}]} 926 | {"id": "00926", "query": "对邓丽君唱法的最佳演绎——北京姑娘陈佳歌曲串烧——影视风云20150415纪念邓丽君逝世二十周年节目剪辑", "TV": [{"str": "对", 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"七龙珠53", "TV": [{"str": "七龙珠", "start_position": 0, "end_position": 2}]} 933 | {"id": "00933", "query": "【幻樱字幕组】NHK纪录片_历史秘话_明治恶妻传说新岛八重的一生", "TV": [{"str": "历史秘话", "start_position": 14, "end_position": 17}, {"str": "明治恶妻", "start_position": 19, "end_position": 22}, {"str": "新岛八重", "start_position": 25, "end_position": 28}]} 934 | {"id": "00934", "query": "歌手寒武纪为河南千娇百媚送上的真挚祝福", "TV": [{"str": "歌手寒武纪", "start_position": 0, "end_position": 4}]} 935 | {"id": "00935", "query": "《何以笙箫默》”二哥“特辑黄晓明被爆”最爱拍床戏“", "TV": [{"str": "何以笙箫默", "start_position": 1, "end_position": 5}], "PER": [{"str": "黄晓明", "start_position": 13, "end_position": 15}]} 936 | {"id": "00936", "query": "速度与激情7.BD1280高清中字.rmvb"} 937 | {"id": "00937", "query": "档案张行2015年4月28日", "TV": [{"str": "档案张行", "start_position": 0, "end_position": 3}]} 938 | {"id": "00938", "query": "月赛半决赛小枫队VS鸽子男第2场", "TV": [{"str": "月", "start_position": 0, "end_position": 0}, {"str": "小枫队", "start_position": 5, "end_position": 7}, {"str": "鸽子男", "start_position": 10, "end_position": 12}], "NUM": [{"str": "第2场", "start_position": 13, "end_position": 15}]} 939 | {"id": "00939", "query": "《跑男2》0501期邓超不爽和老婆夫妻相：侮辱了我自己！2015511", "PER": [{"str": "邓超", "start_position": 10, "end_position": 11}]} 940 | {"id": "00940", "query": "火影金身鸣人", "TV": [{"str": "火影金身鸣人", "start_position": 0, "end_position": 5}]} 941 | {"id": "00941", "query": "《爱情保卫战》20150415自负男嫌女友“土搓圆“高清"} 942 | {"id": "00942", "query": "水月洞天28", "TV": [{"str": "水月洞天", "start_position": 0, "end_position": 3}]} 943 | {"id": "00943", "query": "速度与激情片尾曲1"} 944 | {"id": "00944", "query": "①西游记中观音到底是男是女？", "TV": [{"str": "①", "start_position": 0, "end_position": 0}, {"str": "西游记", "start_position": 1, "end_position": 3}, {"str": "观音", "start_position": 5, "end_position": 6}]} 945 | {"id": "00945", "query": "1986版西游记21"} 946 | {"id": "00946", "query": "《非常完美》20150411台妹现场测男嘉宾腹肌", "TV": [{"str": "非常完美", "start_position": 1, "end_position": 4}], "PER": [{"str": "台妹", "start_position": 14, "end_position": 15}]} 947 | {"id": "00947", "query": "唐嫣从乱牙小黑妹整形变甜美08", "PER": [{"str": "唐嫣", "start_position": 0, "end_position": 1}]} 948 | {"id": "00948", "query": "黄晓明霸气扑倒郑恺轻松俘虏情敌心150424奔跑吧兄弟", "PER": [{"str": "黄晓明", "start_position": 0, "end_position": 2}, {"str": "郑恺", "start_position": 7, "end_position": 8}], "TV": [{"str": "奔跑吧兄弟", "start_position": 22, "end_position": 26}]} 949 | {"id": "00949", "query": "小黄人版咱当兵的人！", "TV": [{"str": "小黄人", "start_position": 0, "end_position": 2}]} 950 | {"id": "00950", "query": "[未播片段]郑恺难过baby美人关邓超喝醉乱改歌词", "PER": [{"str": "郑恺", "start_position": 6, "end_position": 7}, {"str": "邓超", "start_position": 17, "end_position": 18}]} 951 | {"id": "00951", "query": "《特警力量》大结局片段_高清", "TV": [{"str": "特警力量", "start_position": 1, "end_position": 4}]} 952 | {"id": "00952", "query": "《我是演员之武侠剧》（完整版）：宋小宝+小沈阳", "PER": [{"str": "宋小宝", "start_position": 16, "end_position": 18}, {"str": "小沈阳", "start_position": 20, "end_position": 22}]} 953 | {"id": "00953", "query": "恐龙战队第五季01", "TV": [{"str": "恐龙战队", "start_position": 0, "end_position": 3}], "NUM": [{"str": "第五季", "start_position": 4, "end_position": 6}]} 954 | {"id": "00954", "query": "海贼王第123話追上砂鳄鱼！路飞向王家的墓地冲去！", "TV": [{"str": "砂鳄鱼", "start_position": 10, "end_position": 12}], "PER": [{"str": "路飞", "start_position": 14, "end_position": 15}]} 955 | {"id": "00955", "query": "TFBOYS东方风云榜颁奖典礼台下花絮与金星互动by大刀", "TV": [{"str": "东方风云榜", "start_position": 6, "end_position": 10}], "PER": [{"str": "金星", "start_position": 20, "end_position": 21}, {"str": "by", "start_position": 24, "end_position": 25}]} 956 | {"id": "00956", "query": "犬夜叉第34集", "TV": [{"str": "犬夜叉", "start_position": 0, "end_position": 2}]} 957 | {"id": "00957", "query": "刘德华冰雨", "PER": [{"str": "刘德华", "start_position": 0, "end_position": 2}, {"str": "冰雨", "start_position": 3, "end_position": 4}]} 958 | {"id": "00958", "query": "名侦探柯南247", "TV": [{"str": "名侦探柯南", "start_position": 0, "end_position": 4}]} 959 | {"id": "00959", "query": "生化危机：恶化　预告片动画版", "TV": [{"str": "生化危机", "start_position": 0, "end_position": 3}]} 960 | {"id": "00960", "query": "警察故事", "TV": [{"str": "警察故事", "start_position": 0, "end_position": 3}]} 961 | {"id": "00961", "query": "死神154", "TV": [{"str": "死神", "start_position": 0, "end_position": 1}]} 962 | {"id": "00962", "query": "火影忍者第250话", "TV": [{"str": "火影忍者", "start_position": 0, "end_position": 3}]} 963 | {"id": "00963", "query": "王颖母子相声", "PER": [{"str": "王颖", "start_position": 0, "end_position": 1}]} 964 | {"id": "00964", "query": "名侦探柯南463", "TV": [{"str": "名侦探柯南", "start_position": 0, "end_position": 4}]} 965 | {"id": "00965", "query": "火影忍者333", "TV": [{"str": "火影忍者", "start_position": 0, "end_position": 3}]} 966 | {"id": "00966", "query": "钢铁三国志18", "TV": [{"str": "钢铁三国志", "start_position": 0, "end_position": 4}]} 967 | {"id": "00967", "query": "冰河世纪3恐龙的黎明预告片1", "TV": [{"str": "冰河世纪", "start_position": 0, "end_position": 3}, {"str": "恐龙的黎明", "start_position": 5, "end_position": 9}]} 968 | {"id": "00968", "query": "犬夜叉128"} 969 | {"id": "00969", "query": "网球王子120", "TV": [{"str": "网球王子", "start_position": 0, "end_position": 3}]} 970 | {"id": "00970", "query": "海贼王406"} 971 | {"id": "00971", "query": "名侦探柯南[486]从右到左的招财猫", "TV": [{"str": "名侦探柯南", "start_position": 0, "end_position": 4}]} 972 | {"id": "00972", "query": "仙剑奇侠传三05", "TV": [{"str": "仙剑奇侠传", "start_position": 0, "end_position": 4}]} 973 | {"id": "00973", "query": "网球王子137", "TV": [{"str": "网球王子", "start_position": 0, "end_position": 3}]} 974 | {"id": "00974", "query": "七龙珠全集097龙争虎斗(第一部)", "TV": [{"str": "七龙珠", "start_position": 0, "end_position": 2}, {"str": "龙争虎斗", "start_position": 8, "end_position": 11}], "NUM": [{"str": "第一部", "start_position": 13, "end_position": 15}]} 975 | {"id": "00975", "query": "OnePiece海贼王135"} 976 | {"id": "00976", "query": "海贼王167"} 977 | {"id": "00977", "query": "死神145", "TV": [{"str": "死神", "start_position": 0, "end_position": 1}]} 978 | {"id": "00978", "query": "PARADISE神话"} 979 | {"id": "00979", "query": "死神93", "TV": [{"str": "死神", "start_position": 0, "end_position": 1}]} 980 | {"id": "00980", "query": "韩剧《做得好做得妙》全31集", "TV": [{"str": "做得好", "start_position": 3, "end_position": 5}, {"str": "做得好做得妙", "start_position": 3, "end_position": 8}]} 981 | {"id": "00981", "query": "国产古装神话电视剧《牛郎织女》24", "TV": [{"str": "牛郎织女", "start_position": 10, "end_position": 13}]} 982 | {"id": "00982", "query": "仙剑奇侠传三", "TV": [{"str": "仙剑奇侠传", "start_position": 0, "end_position": 4}]} 983 | {"id": "00983", "query": "海贼王118集"} 984 | {"id": "00984", "query": "火影忍者101(清晰)", "TV": [{"str": "火影忍者", "start_position": 0, "end_position": 3}]} 985 | {"id": "00985", "query": "猫和老鼠.四川方言版.第11辑.3.想要有个窝", "TV": [{"str": "猫和老鼠", "start_position": 0, "end_position": 3}]} 986 | {"id": "00986", "query": "【MAD】【龙珠】～SHUTUPANDEXPLODE～1000人突破＆1周年祭.mp4", "PER": [{"str": "MAD", "start_position": 1, "end_position": 3}], "TV": [{"str": "龙珠", "start_position": 6, "end_position": 7}]} 987 | {"id": "00987", "query": "名侦探柯南004", "TV": [{"str": "名侦探柯南", "start_position": 0, "end_position": 4}]} 988 | {"id": "00988", "query": "数码宝贝第三部24", "NUM": [{"str": "第三部", "start_position": 4, "end_position": 6}]} 989 | {"id": "00989", "query": "猫和老鼠东北版(27)"} 990 | {"id": "00990", "query": "蔡秋凤-你讲我的世界是你流浪的所在", "PER": [{"str": "蔡秋凤", "start_position": 0, "end_position": 2}]} 991 | {"id": "00991", "query": "仙剑奇侠传三之05[高清DVD](霍建华唐嫣)", "TV": [{"str": "仙剑奇侠传", "start_position": 0, "end_position": 4}], "PER": [{"str": "霍建华", "start_position": 17, "end_position": 19}, {"str": "霍建华唐嫣", "start_position": 17, "end_position": 21}]} 992 | {"id": "00992", "query": "变形金刚2高清晰完整版本", "TV": [{"str": "变形金刚", "start_position": 0, "end_position": 3}]} 993 | {"id": "00993", "query": "放羊的星星02", "TV": [{"str": "放羊的星星", "start_position": 0, "end_position": 4}]} 994 | {"id": "00994", "query": "死神Bleach228", "TV": [{"str": "死神", "start_position": 0, "end_position": 1}]} 995 | {"id": "00995", "query": "灌篮高手74"} 996 | {"id": "00996", "query": "蜗居-19"} 997 | {"id": "00997", "query": "刘德华恶搞小沈阳《给小沈阳的歌》", "PER": [{"str": "刘德华", "start_position": 0, "end_position": 2}, {"str": "小沈阳", "start_position": 5, "end_position": 7}, {"str": "小沈阳《给小沈阳", "start_position": 5, "end_position": 12}]} 998 | {"id": "00998", "query": "名侦探柯南第212话", "TV": [{"str": "名侦探柯南", "start_position": 0, "end_position": 4}]} 999 | {"id": "00999", "query": "火影忍者疾风剧场预告", "TV": [{"str": "火影忍者", "start_position": 0, "end_position": 3}, {"str": "疾风", "start_position": 4, "end_position": 5}]} 1000 | {"id": "01000", "query": "火影忍者238", "TV": [{"str": "火影忍者", "start_position": 0, "end_position": 3}]} -------------------------------------------------------------------------------- /submission/eval.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | 4 | def get_entities(ent_dict): 5 | entities = [] 6 | for key in ent_dict.keys(): 7 | if key not in ['id', 'query']: 8 | for ent in ent_dict[key]: 9 | entities.append((ent['start_position'], ent['end_position'], key)) 10 | return entities 11 | 12 | 13 | def evaluate(f_name_pred, f_name_true): 14 | file_pred = open(f_name_pred, 'r', encoding='utf-8').read().split('\n') 15 | file_true = open(f_name_true, 'r', encoding='utf-8').read().split('\n') 16 | dicts_pred, dicts_true = [], [] 17 | 18 | for line in file_pred: 19 | if len(line) == 0: 20 | continue 21 | else: 22 | dicts_pred.append(json.loads(line)) 23 | 24 | for line in file_true: 25 | if len(line) == 0: 26 | continue 27 | else: 28 | dicts_true.append(json.loads(line)) 29 | 30 | assert len(dicts_pred) == len(dicts_true) 31 | dicts_pred.sort(key=lambda a: a['id']) 32 | dicts_true.sort(key=lambda a: a['id']) 33 | 34 | true, pred, right = 0, 0, 0 35 | 36 | for i in range(len(dicts_pred)): 37 | assert dicts_pred[i]["query"] == dicts_true[i]["query"] 38 | entities_pred = list(set(get_entities(dicts_pred[i]))) 39 | entities_true = list(set(get_entities(dicts_true[i]))) 40 | pred += len(entities_pred) 41 | true += len(entities_true) 42 | for one in entities_pred: 43 | if one in entities_true: 44 | right += 1 45 | precision = right * 1.0 / pred if pred > 0 else 0 46 | recall = right * 1.0 / true if true > 0 else 0 47 | f1 = (2.0 * precision * recall) / (precision + recall) if precision > 0 or recall > 0 else 0 48 | return precision, recall, f1 49 | 50 | 51 | if __name__ == '__main__': 52 | fname_pred = 'YourTeamName.json' 53 | fname_true = 'ValidLabel.json' 54 | p, r, f1 = evaluate(fname_pred, fname_true) 55 | print(p * 100, r * 100, f1 * 100) 56 | 57 | --------------------------------------------------------------------------------