Automation

A Python package to facilitate research on building and evaluating automated scoring models

Automated scoring of written and spoken test responses is a growing field in educational natural language processing. Automated scoring engines employ machine learning models to predict scores for such responses based on features extracted from the text/audio of these responses. Examples of automated scoring engines include Project Essay Grade for written responses and SpeechRater for spoken responses. Rater Scoring Modeling Tool (RSMTool) is a python package which automates and combines in a single pipeline multiple analyses that are commonly conducted […]

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Face recognition

A lightweight face-recognition toolbox and pipeline based on tensorflow-lite

FaceIDLight A lightweight face-recognition toolbox and pipeline based on tensorflow-lite with MTCNN-Face-Detection and ArcFace-Face-Recognition. No need to install complete tensorflow, tflite-runtime is enough. All tools are using CPU only. 📘 Description A lightweight face-recognition toolbox and pipeline based on tensorflow-lite with MTCNN-Face-Detectionand ArcFace-Face-Recognition. No need to install complete tensorflow, tflite-runtime is enough. All tools areusing CPU only. Pull request are welcome! ⚡️ Features Online Face-Recognition Running completely on CPU Multi Faces ~4 FPS on a MacBookPro2015 Tools for Face-Detection, -Verification […]

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Excel

Accelerated Excel XLSX Writing Library for Python 2/3

PyExcelerate Accelerated Excel XLSX writing library for Python PyExcelerate is a Python for writing Excel-compatible XLSX spreadsheet files, with an emphasis on speed. Benchmarks Benchmark code located in pyexcelerate/tests/benchmark.pyUbuntu 12.04 LTS, Core i5-3450, 8GB DDR3, Python 2.7.3 | TEST_NAME | NUM_ROWS | NUM_COLS | TIME_IN_SECONDS | |—————————–|———-|———-|—————–| | pyexcelerate value fastest | 1000 | 100 | 0.47 | | pyexcelerate value faster | 1000 | 100 | 0.51 | | pyexcelerate value fast | 1000 | 100 | 1.53 | […]

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Deep Learning

A library for Deep Learning Implementations and utils

deeply A library for Deep Learning Implementations and utils. Features Installation $ pip install git+https://github.com/achillesrasquinha/deeply.git Usage Application Interface >>> import deeply Command-Line Interface $ deeply Usage: deeply [OPTIONS] COMMAND [ARGS]… A Deep Learning library Options: –version Show the version and exit. -h, –help Show this message and exit. Commands: help Show this message and exit. version Show version and exit. GitHub https://github.com/achillesrasquinha/deeply    

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Server

A web GUI to manage and monitorize your Wireguard server

Linguard Linguard aims to provide an easy way to manage your WireGuard server, and it’s written in Python3 and powered by Flask. Screenshots Installation Git Install dependencies: sudo apt update sudo apt install wireguard iptables uwsgi uwsgi-plugin-python3 libpcre3 libpcre3-dev Download any release (or clone the repository) and put the files somewhere you will remember later, such as /var/www/linguard. Edit the configuration files to fit your needs. Add a linguard user and group to your computer: groupadd linguard useradd -g linguard […]

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Wrapper

Wrapper for the undocumented CodinGame API

codingame API wrapper Pythonic wrapper for the undocumented CodinGame API. Installation Python 3.6 or higher is required. Install codingame with pip: pip install codingame Quickstart Create an application, in example.py: import codingame client = codingame.Client() # if you want to log in client.login(“[email protected]”, “password”) # get a codingamer codingamer = client.get_codingamer(“username”) print(codingamer.pseudo) # get the global leaderboard global_leaderboard = client.get_global_leaderboard() # print the pseudo of the top codingamer print(global_leaderboard.users[0].pseudo) GitHub https://github.com/takos22/codingame    

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Dataset

A python library that generates random facts

Randfacts Randfacts is a python library that generates random facts. You can use randfacts.get_fact() to return a random fun fact. Disclaimer: Facts are not guaranteed to be true. randfacts can either be installed via pip or via the AUR, whichever way you prefer. Installation via pip: $ pip3 install randfacts Installation via AUR: $ git clone https://aur.archlinux.org/python-randfacts.git && cd python-randfacts $ makepkg -si import randfacts x = randfacts.get_fact() print(x) will print a random fact like:Penguins can’t taste sweet or savory […]

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Automation

Python library for parsing Godot scene files

This is a python library for parsing Godot scene (.tscn) and resource (.tres) files. It’s intended to make it easier to automate certain aspects of editing scene files or resources in Godot. High-level API godot_parser has roughly two levels of API. The low-level API has no Godot-specific logic and is just a dumb wrapper for the file format. The high-level API has a bit of application logic on top to mirror Godot functionality and make it easier to perform certain […]

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Workflow

Python client for using Prefect Cloud with Saturn Cloud

prefect-saturn prefect-saturn is a Python package that makes it easy to run Prefect Cloud flows on a Dask cluster with Saturn Cloud. For a detailed tutorial, see “Fault-Tolerant Data Pipelines with Prefect Cloud “. Installation prefect-saturn is available on PyPi. pip install prefect-saturn prefect-saturn can be installed directly from GitHub pip install git+https://github.com/saturncloud/[email protected] Getting Started prefect-saturn is intended for use inside a Saturn Cloud environment, such as a Jupyter notebook. import prefect from prefect import Flow, task from prefect_saturn import […]

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Machine Learning

Python Automated Machine Learning library for tabular data

SAP-HANA-AutoML Simple but powerful Automated Machine Learning library for tabular data. It uses efficient in-memory SAP HANA algorithms to automate routine Data Science tasks. Disclaimer This library is an open-source research project and is not part of any official SAP products. What’s this? This is a simple but accurate Automated Machine Learning library. Based on SAP HANA powerful in-memory algorithms, it provides high accuracy in multiple machine learning tasks. Our library also uses numerous data preprocessing functions to automate routine […]

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