Document

A Python3 command line tool and library that helps you create great documentation websites

Read Latest Documentation – Browse GitHub Code Repository The only thing worse than documentation never written, is documentation written but never discovered. portray is a Python3 command line tool and library that helps you create great documentation websites for your Python projects with as little effort as possible. Key Features: Zero-Config: No configuration is necessary to use portray. Just run portray in    

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Documentation

Automatic documentation from sources for MkDocs

Automatic documentation from sources, for MkDocs. Features Language agnostic: just like mkdocs, mkdocstrings is written in Python but is language-agnostic. It means you can use it for any language, as long as you implement a handler for it. Currently, we only have a Python handler. Maybe you’d like to contribute another one?? Multiple themes support: each handler can offer multiple themes. Currently, we offer the⭐Material theme⭐as well as basic support for the ReadTheDocs theme for the Python handler. Cross-references to […]

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MKDocs

MkDocs plugin to export content pages as PDF files

An MkDocs plugin to export content pages as PDF files The pdf-export plugin will export all markdown pages in your MkDocs repository as PDF files using WeasyPrint. The exported documents support many advanced features missing in most other PDF exports, such as a PDF Index and support for CSS paged media module. Requirements This package requires MkDocs version 1.0 or higher (0.17 works as well) Python 3.4 3.5 or higher WeasyPrint depends on cairo, Pango and GDK-PixBuf which need to […]

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MKDocs

MkDocs plugin that simplifies configuring page titles and their order

An MkDocs plugin that simplifies configuring page titles and their order The awesome-pages plugin allows you to customize how your pages show up the navigation of your MkDocs without having to configure the full structure in your mkdocs.yml. It gives you detailed control using a small configuration file directly placed in the relevant directory of your documentation. Note: This plugin won’t do anything if your mkdocs.yml defines a nav or pages entry. To make use of the features listed below, […]

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Documentation

Run black on python code blocks in documentation files

Run black on python code blocks in documentation files. install pip install blacken-docs usage blacken-docs provides a single executable (blacken-docs) which will modify .rst / .md / .tex files in place. It currently supports the following black options: -l / –line-length -t / –target-version -S / –skip-string-normalization Following additional parameters can be used: blacken-docs will format code in the following block types: (markdown) “`python def hello(): print(“hello world”) “` (rst)    

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ddos

Shitty DDoS Script With Python

This is a bad ddos tool script i made, there are tons of tools 100 times better but i’ll try to improve this and make it public here Just change Target Ip Port and choose the thread numbers Script 1 The first script just realizes the attack but there’s no way to know if it worked Script 2 This script realizes the attack and prints the amount of packets sent to the attacked Script 3 Script 2 but improved so […]

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Parsing

CONFIDENT SEMANTIC RANKING LOSS FOR PART PARSING

If training from scratch, simply run. If not, customize the dir in /experiments/CSR config.py. @inproceedings{tan2021confident, title={Confident Semantic Ranking Loss for Part Parsing}, author={Tan, Xin and Xu, Jiachen and Ye, Zhou and Hao, Jinkun and Ma, Lizhuang}, booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)}, pages={1–6}, year={2021}, organization={IEEE} }    

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Sentiment

Aspect Sentiment Quad Prediction (ASQP)

We highly recommend you to install the specified version of the following packages to avoid unnecessary troubles: If the code is used in your research, please star our repo and cite our paper as follows: @inproceedings{zhang-etal-2021-aspect-sentiment, title = “Aspect Sentiment Quad Prediction as Paraphrase Generation”, author = “Zhang, Wenxuan and Deng, Yang and Li, Xin and Yuan, Yifei and Bing, Lidong and Lam, Wai”, booktitle =    

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PyTorch

Distributed Arcface Training in Pytorch

This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millionsidentity on a single server. Requirements How to Training To train a model, run train.py with the path to the configs: 1. Single node, 8 GPUs: python -m torch.distributed.launch –nproc_per_node=8 –nnodes=1 –node_rank=0 –master_addr=”127.0.0.1″ –master_port=1234 train.py configs/ms1mv3_r50 2. Multiple nodes, each node 8 GPUs: Node 0: python -m torch.distributed.launch –nproc_per_node=8 –nnodes=2 –node_rank=0 –master_addr=

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