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Li Jing's Projects

a-stock-prediction-algorithm-based-on-machine-learning icon a-stock-prediction-algorithm-based-on-machine-learning

(陆续更新)重新整理过的基于机器学习的股票价格预测算法,里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测,包含:LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM、随机森林等

bdci_car_2018 icon bdci_car_2018

BDCI 2018 汽车行业用户观点主题及情感识别 决赛一等奖方案

bert icon bert

TensorFlow code and pre-trained models for BERT

bert_textmatching icon bert_textmatching

利用预训练的中文模型实现基于bert的语义匹配模型 数据集为LCQMC官方数据

bertopic icon bertopic

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

claff-offmychest icon claff-offmychest

Corpus and annotations for the CL-Aff Shared Task - Get it #OffMyChest - from the University of Pennsylvania and Nanyang Technological University Singapore

cnn-lstm-attention icon cnn-lstm-attention

使用卷积神经网络-长短期记忆网络(bi-LSTM)-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.

crimekgassitant icon 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.

embedding_study icon embedding_study

中文预训练模型生成字向量学习,测试BERT,ELMO的中文效果

erc icon erc

Emotion recognition in conversation

fddc02_intro icon fddc02_intro

【梳理】FDDC2018金融算法挑战赛02-A股上市公司公告信息抽取

financialdatasets icon financialdatasets

SmoothNLP 金融文本数据集(公开) Public Financial Datasets for NLP Researches Only

gctm icon gctm

This is an implementation of Graph Convolution Topic Model for Data Streams

inverseprompting icon inverseprompting

Code, Data and Demo for Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting

lihang icon lihang

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

medical-named-entity-recognition icon medical-named-entity-recognition

本项目是针对医疗数据,进行命名实体识别。项目中有600份标注好的电子病历文本,共需识别含解剖部位、独立症状、症状描述、手术和药物五类实体。该领域的命名实体识别问题是自然语言处理中经典的序列标注问题。

medicalnamedentityrecognition icon 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.

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