Wang-Haining

Wang-Haining

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Company:Indiana University Bloomington

Location:Bloomington

Home Page:hainingwang.org

Twitter:@Haining_Wang_

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Wang-Haining's repositories

100_english_novels

A benchmark corpus of 100 English novels, covering the 19th and the beginning of the 20th century

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adversarial_decomposition

The code for the paper "Adversarial Decomposition of Text Representation", NAACL 2019

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ancient_chinese_sense_annotation

Ancient Chinese Corpus with Word Sense Annotation

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noveval

Automatically Evaluating Scholarly Novelty in Alignment with Human Assessments

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RLAM

Reinforcement Learning from Accessibility Measures

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scholarly_abstract_simplification

This repository accompanies our manuscript Simplifying Scholarly Abstracts for Accessible Digital Libraries submitted to JCDL2024.

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Awesome-Diffusion-Models

A collection of resources and papers on Diffusion Models

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blank-peer-review-forms-nlp

A collection of blank peer-review forms for various NLP venues and journals

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chinese-gushiwen

古文诗歌一万语料库

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chinese-stroke

汉字笔顺

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Classical-Modern

非常全的文言文(古文)-现代文平行语料

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handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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lightning-transformers

Flexible components pairing 🤗 Transformers with Pytorch Lightning

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pile-allpoetry

Scraper to gather poems and associated comments from allpoetry.com

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ppl_nanoGPT_stale

The simplest, fastest repository for training/finetuning medium-sized GPTs.

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RLUAM

Repo for reproducing Reinforcement Learning from Uncombined Accessibility Measures.

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s2orc-doc2json

Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON)

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stat453-deep-learning-ss21

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)

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streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

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TransformerCVAE

Transformer-based Conditional Variational Autoencoder for Controllable Story Generation

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transformers-into-vaes

Code for "Finetuning Pretrained Transformers into Variational Autoencoders"

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