hongjiedai's repositories
a-PyTorch-Tutorial-to-Sequence-Labeling
Empower Sequence Labeling with Task-Aware Neural Language Model | a PyTorch Tutorial to Sequence Labeling
AttentionDeepMIL
Implementation of Attention-based Deep Multiple Instance Learning in PyTorch
DeepLearning-MuLi-Notes
Notes about courses Dive into Deep Learning by Mu Li
Digital-Twin-in-python
In this repo we will show how to build a simple but useful Digital Twin using python. Our asset will be a Li-ion battery. This Digital Twin will allow us to model and predict batteries behavior and can be included in any virtual asset management process.
Electrical-Vehicle-Motor-Temperature-Rise-Prediction
MACHINE LEARNING PROJECT
Electrical-vehicles-data-analytics-and-price-prediction
Electrical vehicles data analytics and price prediction
hello-world
hello-world
ML-Papers-Explained
Explanation to key concepts in ML
Extracting-Training-Data-from-Large-Langauge-Models
A re-implementation of the "Extracting Training Data from Large Language Models" paper by Carlini et al., 2020
gpt-pytorch
PyTorch Implementation of OpenAI GPT
GPT2-Chinese
Chinese version of GPT2 training code, using BERT tokenizer.
LM_Memorization
Training data extraction on GPT-2
medmcqa
A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.
openwebtext
Open clone of OpenAI's unreleased WebText dataset scraper. This version uses pushshift.io files instead of the API for speed.
pytorch-nlp-notebooks
Learn how to use PyTorch to solve some common NLP problems with deep learning.
RAMP-mobility
A novel application of the RAMP main engine for generating bottom-up stochastic electric vehicles load profiles.
transformers-tutorials
Github repo with tutorials to fine tune transformers for diff NLP tasks
twlm
Taiwanese Mandarin LLM Project
uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2022/Spring 2022
visimportance
Code to supplement the paper "Learning Visual Importance for Graphic Designs and Data Visualizations" [UIST'17]