lionape

Python

All Algorithms implemented in Python

bert

TensorFlow code and pre-trained models for BERT

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

pytorch-examples

Simple examples to introduce PyTorch

BERT-BiLSTM-CRF-NER

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

tensorflow-windows-wheel

Tensorflow prebuilt binary for Windows

zh-NER-TF

A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)

named_entity_recognition

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

sequence_tagging

Named Entity Recognition (LSTM + CRF) - Tensorflow

NCRFpp

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

LatticeLSTM

Chinese NER using Lattice LSTM. Code for ACL 2018 paper.

BERT-NER

Pytorch-Named-Entity-Recognition-with-BERT

NER

基于tensorflow深度学习的中文的命名实体识别

weiboanalysis

微博情感分析，文本分类，毕业设计项目

LM-LSTM-CRF

Empower Sequence Labeling with Task-Aware Language Model

Weibo-Analyst

Social media (Weibo) comments analyzing toolbox in Chinese 微博评论分析工具, 实现功能: 1.微博评论数据爬取; 2.分词与关键词提取; 3.词云与词频统计; 4.情感分析; 5.主题聚类

Name-Entity-Recognition

Lstm-crf,Lattice-CRF,bert-ner及近年ner相关论文follow

LexiconAugmentedNER

Reject complicated operations for incorporating lexicon for Chinese NER.

BERT-CH-NER

基于BERT的中文命名实体识别

NER-LSTM-CRF

An easy-to-use named entity recognition (NER) toolkit, implemented the Bi-LSTM+CRF model in tensorflow.

NER

CWS_Dict

Source codes for paper "Neural Networks Incorporating Dictionaries for Chinese Word Segmentation", AAAI 2018

BERT_ChineseWordSegment

A Chinese word segment model based on BERT, F1-Score 97%

Named-Entity-Recognition

nlp_lab

Code lab for NLP. Including doc2txt,tf-idf,cnn,text classify,hmm cws,crf ner.

CWS_Dict_comment

为 AAAI-2018 Neural Networks Incorporating Dictionaries for Chinese Word Segmentation 添加大量注释