qq_37418807的博客

项目运行效果:训练:测试:代码:项目结构:1.config.pyclass Config(object): '''配置类''' def __init__(self): self.dataset = 'poety' self.label_file = './data/{}/example.train'.format(self.dataset) self.train_file = './data/{}/example.train

from seqeval.metrics import f1_scorefrom seqeval.metrics import precision_scorefrom seqeval.metrics import accuracy_scorefrom seqeval.metrics import recall_scorefrom seqeval.metrics import classification_reporty_true = [['O', 'O', 'O', 'B-MISC', 'I-MI

def check_bio(tags): """ 检测输入的tags是否是bia编码 如果不是bio编码 那么错误的类型: (1)编码不在BIO中 (2)第一个编码是I (3)当前编码不是B,前一个编码不是O """ for i,tag in enumerate(tags): if tag == 'O': continue tag_list = tag.split('-')

from estimate import Precision,Recall,F1_scoreimport sysimport torchimport timefrom tqdm import tqdmfrom torch.utils.data import TensorDataset,DataLoadersys.path.append('..')from model.BERT_BiLSTM_CRF import BERT_BiLSTM_CRFfrom config import Config

import torchimport osimport timeimport torch.nn as nnimport syssys.path.append('..')from tqdm import tqdmfrom model.BERT_BiLSTM_CRF import BERT_BiLSTM_CRFfrom main import validclass InputFeature(object): def __init__(self,input_id,label_id,inp

案例简介模型输入:seq:[batch_size,len_seq,num_tags]seq_tag:[batch_size,len_seq]模型输出:likehood:tensor()案例代码import torchfrom TorchCRF import CRFfrom torch.utils.data import TensorDataset,DataLoaderfrom torch.optim import Adamimport matplotlib.pyplot as plt