Pinned Repositories
LEVEN
Source code and dataset for ACL2022 Findings Paper "LEVEN: A Large-Scale Chinese Legal Event Detection dataset"
tensorflowdocs
tf docs
ai_technology
人工智能技术作业及资源共享
linux-exp
linux experiment
annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
bert
TensorFlow code and pre-trained models for BERT
CAIL2019
CAIL2022
CapsNet-Pytorch
Pytorch version of Hinton's Capsule Theory paper: Dynamic Routing Between Capsules
CapsNet-Tensorflow
A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules
hjmzy's Repositories
hjmzy/ChatRWKV
ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
hjmzy/RWKV-LM
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
hjmzy/annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
hjmzy/LLMSurvey
The official GitHub page for the survey paper "A Survey of Large Language Models".
hjmzy/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
hjmzy/curai-research
hjmzy/chatgpt_academic
科研工作专用ChatGPT拓展,特别优化学术Paper润色体验,支持自定义快捷按钮,支持markdown表格显示,Tex公式双显示,代码显示功能完善,新增本地Python工程剖析功能/自我剖析功能
hjmzy/carrot
这儿为你准备了众多免费好用的ChatGPT镜像站点,当前81个点
hjmzy/stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.
hjmzy/self-instruct
Aligning pretrained language models with instruction data generated by themselves.
hjmzy/LEVEN
Source code and dataset for ACL2022 Findings Paper "LEVEN: A Large-Scale Chinese Legal Event Detection dataset"
hjmzy/GENRE
Autoregressive Entity Retrieval
hjmzy/EventGraph
hjmzy/lshort
The Not So Short Introduction to LaTeX
hjmzy/DeepKE
An Open Toolkit for Knowledge Graph Extraction and Construction published at EMNLP2022 System Demonstrations
hjmzy/CAIL2022
hjmzy/lshort-zh-cn
A Chinese edition of the Not So Short Introduction to LaTeX2ε
hjmzy/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
hjmzy/UIE
Unified Structure Generation for Universal Information Extraction
hjmzy/MarkStudio
中文标注工具,支持NER、文本分类、关系标注、对话标注等。
hjmzy/Text2Event
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
hjmzy/Paddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
hjmzy/PaddleNLP
Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.
hjmzy/dygiepp
Span-based system for named entity, relation, and event extraction.
hjmzy/Eider
Source code for paper "EIDER: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion", ACL Findings, 2022
hjmzy/ML2022-Spring
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2022 Spring
hjmzy/MAVEN-dataset
Source code and dataset for EMNLP 2020 paper "MAVEN: A Massive General Domain Event Detection Dataset".
hjmzy/jerex
PyTorch code for JEREX: Joint Entity-Level Relation Extractor
hjmzy/HySPA
[ACL 2021 Findings] HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
hjmzy/naive-ui
A Vue 3 Component Library. Fairly Complete. Customizable Themes. Uses TypeScript. Not too Slow.