Akakaala's repositories
WoBERT
以词为基本单位的中文BERT
Chinese-Text-Classification-Pytorch
中文文本分类,TextCNN,TextRNN,FastText,TextRCNN,BiLSTM_Attention,DPCNN,Transformer,基于pytorch,开箱即用。
BERT_BiLSTM_CRF-model
Deep Keyphrase extraction using BiLSTM + CRF , using BERT embeddings
BERT-keyphrase-extraction
Keyphrase Extraction based on Scientific Text, Semeval 2017, Task 10
MatchZoo
Facilitating the design, comparison and sharing of deep text matching models.
sentence-similarity
中文文本句对相似度匹配-ATEC数据集
SIFRank_zh
基于预训练模型的中文关键词抽取方法(论文SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-trained Language Model 的中文版代码)
BERT-BiLSTM-CRF-NER-pytorch
Pytorch BERT-BiLSTM-CRF For NER
CLUEDatasetSearch
搜索所有中文NLP数据集,附常用英文NLP数据集
Text_Matching
文本相似度计算/文本匹配
Bert-Chinese-Text-Classification-Pytorch
使用Bert,ERNIE,进行中文文本分类
text_matching-1
文本匹配的相关模型DSSM,ESIM,ABCNN,BIMPM等,数据集为LCQMC官方数据 可能存在侵权嫌隙数据已删除!
NER_DEMO
中文命名实体识别NER。用keras实现BILSTM+CRF、IDCNN+CRF、BERT+BILSTM+CRF进行实体识别。结果当然是BERT+BILSTM+CRF最好啦。
albert_lstm_crf_ner
albert + lstm + crf实体识别,pytorch实现。识别的主要实体是人名、地名、机构名和时间。
simple-effective-text-matching
Source code of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".
multi-embedding-cws
Multiple Character Embeddings for Chinese Word Segmentation, ACL 2019
NER-Chinese
Comparison of Chinese Named Entity Recognition Models between NeuroNER and BertNER
Sohu2019
2019搜狐校园算法大赛
Bert-BiLSTM-CRF-pytorch
使用谷歌预训练bert做字嵌入的BiLSTM-CRF序列标注模型
deep-relevance-ranking
Deep Relevance Ranking Using Enhanced Document-Query Interactions
crawGovData
爬取政府网站的数据(赣州、吐鲁番、大理、太原、大庆)
ChineseNER
A neural network model for Chinese named entity recognition