YiDai-03 / Chinese_NLP_Dataset
命名实体识别(NER),分词(CWS),实体分类(Entity Typing),关系抽取(Relation Extraction)等任务数据集整理
☆11Updated 5 years ago
Alternatives and similar repositories for Chinese_NLP_Dataset:
Users that are interested in Chinese_NLP_Dataset are comparing it to the libraries listed below
- ☆32Updated 3 years ago
- 中国中文信息学会社会媒体处理专业委员会举办的2019届中文人机对话之自然语言理解竞赛☆74Updated 4 years ago
- code for ACL2020:《FLAT: Chinese NER Using Flat-Lattice Transformer》 我注释&修改&添加了部分源码,使得大家更容易复现这个代码。☆56Updated 4 years ago
- chinese pretrain unilm☆28Updated 4 years ago
- 基于SpanBert的中文指代消解,pytorch实现☆97Updated 2 years ago
- 迭代膨胀卷积命名实体抽取☆45Updated 5 years ago
- 中文版unilm预训练模型☆83Updated 4 years ago
- 利用Bert_CRF进行中文分词☆19Updated 5 years ago
- 《Spelling Error Correction with Soft-Masked BERT》论文复现☆32Updated 3 years ago
- 复现论文《Simplify the Usage of Lexicon in Chinese NER》☆42Updated 3 years ago
- lic2020关系抽取比赛,使用Pytorch实现苏神的模型。☆101Updated 4 years ago
- 基于tensorflow1.x的预训练模型调用,支持单机多卡、梯度累积,XLA加速,混合精度。可灵活训练、验证、预测。☆58Updated 3 years ago
- HMM\CRF\BERT-CRF\BILSTM-CRF\BERTBILSTMCRF\XLNETBILSTMCRF☆33Updated 2 years ago
- 对话改写介绍文章☆95Updated last year
- transformers implement (architecture, task example, serving and more)☆95Updated 3 years ago
- pytorch版的命名实体识别,LSTM和LSTM_CRF☆25Updated 5 years ago
- SIGHAN中文纠错数据集及转换后格式☆62Updated 5 years ago
- 句子匹配模型,包括无监督的SimCSE、ESimCSE、PromptBERT,和有监督的SBERT、CoSENT。☆98Updated 2 years ago
- ☆88Updated 3 years ago
- CGED & CSC☆22Updated 5 years ago
- 机器检索阅读联合学习,莱斯杯:全国第二届“军事智能机器阅读”挑战赛 rank6 方案☆127Updated 4 years ago
- ☆127Updated 2 years ago
- 基于BERT的无监督分词和句法分析☆110Updated 4 years ago
- 关键词抽取项目☆24Updated 4 years ago
- pytorch Efficient GlobalPointer☆53Updated 2 years ago
- 这是使用pytoch 实现的长文本分类器☆45Updated 5 years ago
- Dynamic Connected Networks for Chinese Spelling Check☆50Updated last year
- The dataset and the evaluation tool for NLPCC2018 Shared Task2--Grammatical Error Correction (GEC).☆55Updated 3 years ago
- 5st place solution for competition Duplication Question Detection based on Adversarial Attack☆39Updated 5 years ago
- NLP实验:新词挖掘+预训练模型继续Pre-training☆47Updated last year