zhaoerchao

pyecharts

🎨 Python Echarts Plotting Library

LeakGAN

The codes of paper "Long Text Generation via Adversarial Training with Leaked Information" on AAAI 2018. Text generation using GAN and Hierarchical Reinforcement Learning.

generators-with-stylegan2

Here is a series of face generators based on StyleGAN2

stylegan2

StyleGAN2 - Official TensorFlow Implementation

SinGAN

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

CopyMTL

AAAI20 "CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning"

chinese-chatbot-corpus

中文公开聊天语料库

PreSumm

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

ZEN

A BERT-based Chinese Text Encoder Enhanced by N-gram Representations

BertSum

Code for paper Fine-tune BERT for Extractive Summarization

OpenKP

Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

nlp_chinese_corpus

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

bert_language_understanding

Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN

esim-response-selection

ESIM for Multi-turn Response Selection Task

xlnet

XLNet: Generalized Autoregressive Pretraining for Language Understanding

SogouMRCToolkit

This toolkit was designed for the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.

LAMB_Optimizer_TF

LAMB Optimizer for Large Batch Training (TensorFlow version)

clearml

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

kg-2019

2019年百度的三元组抽取比赛，“科学空间队”源码

FUNIT

Translate images to unseen domains in the test time with few example images.

sru

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

darksight

Interpreting Deep Classifier by Visual Distillation of Dark Knowledge

bertviz

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

mt-dnn

Multi-Task Deep Neural Networks for Natural Language Understanding

vue

This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core

code_snippets

san_mrc

Stochastic Answer Networks (SAN) for Machine Reading Comprehension

KataGo

GTP engine and self-play learning in Go

jupytext

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

AdaBound

An optimizer that trains as fast as Adam and as good as SGD.