syf123's Projects
12 Weeks, 24 Lessons, AI for All!
Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)
Pytorch-Named-Entity-Recognition-with-BERT
Biomedical NLP Tutorial: Introduction to NLP, Named Entity Recognition (NER), and Relation Extraction (RE)
🌞 CareGPT (关怀GPT)是一个医疗大语言模型,同时它集合了数十个公开可用的医疗微调数据集和开放可用的医疗大语言模型,包含LLM的训练、测评、部署等以促进医疗LLM快速发展。Medical LLM, Open Source Driven for a Healthy Future.
ChineseNER based on BERT, with BiLSTM+CRF layer
A PyTorch implementation of a BiLSTM\BERT\Roberta(+CRF) model for Named Entity Recognition.
Config files for my GitHub profile.
通信电子线路设计:小功率调频发射机
采用LTP(分词、词性标注、句法依存、角色标注)抽取事件三元组
An experiment and demo-level tool for text information extraction (event-triples extraction), which can be a route to the event chain and topic graph, 基于依存句法与语义角色标注的事件三元组抽取,可用于文本理解如文档主题链,事件线等应用。
基于知识图谱的问答系统,BERT做命名实体识别和句子相似度,分为online和outline模式
基于知识图谱的《红楼梦》人物关系可视化及问答系统
Langchain-Chatchat 个人开发Repo,主项目请移步 chatchat-space/Langchain-Chatchat
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction (COLING 2022)
The depository support training and testing BERT-CNN model on three medical relation extraction corpora: BioCreative V CDR task corpus, traditional Chinese medicine literature corpus, and i2b2 temporal relation corpus.
MIL-RBERT: A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (BioNLP @ ACL 2020)
NLP 领域常见任务的实现,包括新词发现、以及基于pytorch的词向量、中文文本分类、实体识别、摘要文本生成、句子相似度判断、三元组抽取、预训练模型等。
Code for our ICTA 2023 paper "An Architecture for More Fine-grained Hidden Representation in Named Entity Recognition for Biomedical Texts"
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建,自动问答,基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱,并以该知识图谱完成自动问答与分析服务。
Relation Extraction and NER on BioRED
在非结构化文本中提取三元组
Visual Studio Uninstallation sometimes can be unreliable and often leave out a lot of unwanted artifacts. Visual Studio Uninstaller is designed to thoroughly and reliably remove these unwanted artifacts.