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100-days-of-ml-code

100-Days-Of-ML-Code中文版

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

awesome-bert

bert nlp papers, applications and github resources ， BERT 相关论文和 github 项目

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

bi-lstm-crf

使用keras实现的基于Bi-LSTM + CRF的中文分词+词性标注

ccks-2018-ner

CCKS 2018 面向中文电子病历的命名实体识别

chinesener

中文命名实体识别，实体抽取，tensorflow，pytorch，BiLSTM+CRF

chinesener-1

A neural network model for Chinese named entity recognition

chinesener-based-bert

ChineseNER-based-BERT

cnn_chinesetextbinaryclassify

crimekgassitant

Crime assistant including crime type prediction and crime consult service based on nlp methods and crime kg,罪名法务智能项目,内容包括856项罪名知识图谱, 基于280万罪名训练库的罪名预测,基于20W法务问答对的13类问题分类与法律资讯问答功能.

demo-chinese-text-binary-classification-with-bert

entity-recognition-datasets

A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.

hz-demo

kaggle

Kaggle 项目实战（教程） = 文档 + 代码 + 视频（欢迎参与）

kcws

Deep Learning Chinese Word Segment

lawcrimemining

Law Crime Mining Based on Corpus build and content analysis by NLP methods. 基于领域语料库构建与NLP方法的裁判文书与犯罪案例文本挖掘项目

medicalnamedentityrecognition

Medical Named Entity Recognition implement using bi-directional lstm and crf model with char embedding.CCKS2017中文电子病例命名实体识别项目,主要实现使用了基于字向量的四层双向LSTM与CRF模型的网络.该项目提供了原始训练数据样本(一般醒目,出院情况,病史情况,病史特点,诊疗经过)与转换版本,训练脚本,预训练模型,可用于序列标注研究.把玩和PK使用.

multi-bioner

Cross-type Biomedical Named Entity Recognition with Deep Multi-task Learning (Bioinformatics'19)

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）

nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

pydensecrf

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

relation_extration

Relation Classification via Convolutional Deep Neural Network

yedda

YEDDA: A Lightweight Collaborative Text Span Annotation Tool. Code for ACL 2018 Best Demo Paper Nomination.