Pinned Repositories
AI_Challenger_2018
AI Challenger, a platform for open datasets and programming competitions to artificial intelligence (AI) talents around the world. https://challenger.ai/
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
attention_RNN_for_textsum
This is to reproduce article <A DEEP REINFORCED MODEL FOR ABSTRACTIVE SUMMARIZATION>
BDCI2019-SENTIMENT-CLASSIFICATION
CCF BDCI 2019 互联网新闻情感分析 复赛top1解决方案
bert
TensorFlow code and pre-trained models for BERT
BERT-BiLSTM-CRF-NER
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private server services
BERT-Classify-Pytorch
BERT-Classify-Pytorch
bert-kbqa-NLPCC2017
A trial of kbqa based on bert for NLPCC2016/2017 Task 5 (基于BERT的中文知识库问答实践，代码可跑通）
bert-utils
BERT生成句向量，BERT做文本分类、文本相似度计算
Bidirectiona-LSTM-for-text-summarization-
A bidirectional encoder-decoder LSTM neural network is trained for text summarization on the cnn/dailymail dataset. (MIT808 project)
jkszw2014's Repositories
jkszw2014/bert-kbqa-NLPCC2017
A trial of kbqa based on bert for NLPCC2016/2017 Task 5 (基于BERT的中文知识库问答实践，代码可跑通）
jkszw2014/Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
jkszw2014/AI_Challenger_2018
AI Challenger, a platform for open datasets and programming competitions to artificial intelligence (AI) talents around the world. https://challenger.ai/
jkszw2014/bert
TensorFlow code and pre-trained models for BERT
jkszw2014/BERT-BiLSTM-CRF-NER
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private server services
jkszw2014/BDCI2019-SENTIMENT-CLASSIFICATION
CCF BDCI 2019 互联网新闻情感分析 复赛top1解决方案
jkszw2014/BERT-Classify-Pytorch
BERT-Classify-Pytorch
jkszw2014/bert-utils
BERT生成句向量，BERT做文本分类、文本相似度计算
jkszw2014/Bidirectiona-LSTM-for-text-summarization-
A bidirectional encoder-decoder LSTM neural network is trained for text summarization on the cnn/dailymail dataset. (MIT808 project)
jkszw2014/CDCS
Chinese Data Competitions' Solutions
jkszw2014/Chinese-BERT-wwm
Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）
jkszw2014/chineseGLUE
Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard
jkszw2014/ChineseNLPCorpus
中文自然语言处理数据集，平时做做实验的材料。欢迎补充提交合并。
jkszw2014/CustomerServiceAI
智能客服
jkszw2014/DistilBert
DistilBERT for Chinese 海量中文预训练蒸馏bert模型
jkszw2014/FastBERT
对ACL2020 FastBERT论文的复现，论文地址:https://arxiv.org/pdf/2004.02178.pdf
jkszw2014/FastBERT-1
The score code of FastBERT (ACL2020)
jkszw2014/GraphSAINT
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
jkszw2014/Information-Extraction-Chinese
Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取
jkszw2014/MatchZoo
Facilitating the design, comparison and sharing of deep text matching models.
jkszw2014/nlp_chinese_corpus
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
jkszw2014/Pretrained-Language-Model
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
jkszw2014/rasa
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
jkszw2014/rouge
A full Python Implementation of the ROUGE Metric (not a wrapper)
jkszw2014/tensor2tensor
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
jkszw2014/tensorflow_seq2seq
Tensorflow中的Seq2Seq全家桶
jkszw2014/text-summarization-tensorflow
Tensorflow seq2seq Implementation of Text Summarization.
jkszw2014/text_gcn.pytorch
PyTorch implementation of "Graph Convolutional Networks for Text Classification. Yao et al. AAAI2019."
jkszw2014/TextClassify_with_BERT
使用BERT模型做文本分类；面向工业用途
jkszw2014/TextLevelGCN
source code of our paper presents in EMNLP 2019. https://www.aclweb.org/anthology/D19-1345/