Jack Zhao's repositories
amrlib
A python library that makes AMR parsing, generation and visualization simple.
AstroNvim
AstroNvim is an aesthetic and feature-rich neovim config that is extensible and easy to use with a great set of plugins
bert_score
BERT score for text generation
CloudSimPy
CloudSimPy: Datacenter job scheduling simulation framework
CorrFA_for_Summarizaion
Corr F/A evaluation metrics in paper "Xinnuo Xu, Ondrej Dusek, Jingyi Li, Yannis Konstas, and Verena Rieser. Fact-based Content Weighting for Evaluating Abstractive Summarisation" Proceedings of ACL2020 🎉🎉🎉
cs231n.github.io
Public facing notes page
DataScienceNotes
Temporary notes. Need organization later.
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
emnlp19-moverscore
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
google-research
Google Research
lazyvim_starter
Starter template for LazyVim
llama2
Inference code for LLaMA models
nlp-tutorial-for-dl-researchers
Natural Language Processing Tutorial for Deep Learning Researchers
onestep
A thousand miles begin with a single step.
pandoc-latex-template
A pandoc LaTeX template to convert markdown files to PDF or LaTeX.
practical-nlp-book-code
Official Repository for Code associated with 'Practical Natural Language Processing' book by O'Reilly Media
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
reading-notes
Temporarily storing my reading notes and not for public use.
ReliableSummEvalReg
This repo contains scripts for text summarization evaluation using predictive models.
rlpyt
Reinforcement Learning in PyTorch
sema
An Extended Semantic Evaluation Metric for AMR. Demo version (http://143.107.183.175:23580/sema/)
spinningup
An educational resource to help anyone learn deep reinforcement learning.
SummEval
Resources for the "SummEval: Re-evaluating Summarization Evaluation" paper
URLShortener
This is the answer for the take home test. A Readme file is included about the design and my thought process.