Transformer

Transform-Invariant Non-Negative Matrix Factorization

Transform-Invariant Non-Negative Matrix Factorization A comprehensive Python package for Non-Negative Matrix Factorization (NMF) with a focus on learning transform-invariant representations. The packages supports multiple optimization backends and can be easily extended to handle application-specific types of transforms. A general introduction to Non-Negative Matrix Factorization and the purpose of this package can be found on the corresponding GitHub Pages. For using this package, you will need Python version 3.7 (or higher).The package is available via PyPI. Installation is easiest using pip: […]

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Tool

Library to interact with the lbrynet client from the LBRY project

A library of functions that can be used to manage the download of claims from the LBRY network. It includes methods to download claims by URI (canonical url), claim ID, or from specific channels. It also includes methods to clean up older files and free space, so the functions are suitable for use in a headless computer that will download files, and seed them to the network with little user intervention. This libary is released as free software under the […]

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Monitoring

Performance monitoring and testing of OpenStack

Browbeat Browbeat is a performance tuning and analysis tool for OpenStack. Browbeat is free, Open Source software. Analyze and tune your Cloud for optimal performance. Create Rally workloads for performance and scale testing. Automate deployment of common data analysis tools. Documentation Browbeat documentation is available at https://browbeat.readthedocs.io/ GitHub https://github.com/cloud-bulldozer/browbeat    

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Documentation

A document format conversion service based on Pandoc

reformed Document format conversion service based on Pandoc. Usage The API specification for the Reformed server is as follows: GET /api/v1/formats: Lists available input and output formats for documents Response { “input”: { “commonmark”: { “mime”: “text/markdown”, “ext”: “md”, “detail”: “CommonMark Markdown” }, “docx”: { “mime”: “application/vnd.openxmlformats-officedocument.wordprocessingml.document”, “ext”: “docx”, “detail”: “Word docx” }, // … }, “output”: { “commonmark”: { “mime”: “text/markdown”, “ext”: “md”, “detail”: “CommonMark Markdown” }, “docx”: { “mime”: “application/vnd.openxmlformats-officedocument.wordprocessingml.document”, “ext”: “docx”, “detail”: “Word docx” }, // … […]

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Flask

Library books management web application built with Flask

library books management Web application Library books management web application built with Flask Upload a book to the database, and also see it at home page. You’ll be able to edit the book if you make a mistake while registering it. Borrow a book to the customer and register the borrowed date. You’ll be able to see the borrowed books information. You’ll be able to edit the borrowed books information. You’ll be able to see the returned books information If […]

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Advanced, NLP, Project, Python, Text, Unstructured Data

New Anaphora and Co-reference Resolution Technique for Biographies

This article was published as a part of the Data Science Blogathon Introduction Biographies of many famous personalities are very insightful and inspiring. Although, one may not want to read the whole document. In order to just get the important points from the biography, one can generate a summary of the biography. The summary is generated by giving weights to all the words. Sometimes, anaphoras can be predicted by the machine as a separate word which in return produces a less […]

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Deep Learning, NLP, Project, Python, Unstructured Data

Sentiment Analysis Using Bidirectional Stacked LSTM

This article was published as a part of the Data Science Blogathon Sentiment Analysis Sentiment Analysis is the process of finding the sentiments of the text data. Sentiment Analysis falls under the text classification in Natural Language Processing. Sentiment Analysis would help us to know our customer reviews better. A sentiment denotes any one of the following, Positive, Negative, and Neutral. When we analyze the negative reviews of our products we can easily use those reviews to surmount the problems […]

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Natural Language Processing

Dice Loss for NLP Tasks with python

Dice Loss for NLP Tasks This repository contains code for Dice Loss for Data-imbalanced NLP Tasks at ACL2020. Setup Install Package Dependencies The code was tested in Python 3.6.9+ and Pytorch 1.7.1. If you are working on ubuntu GPU machine with CUDA 10.1, please run the following command to setup environment. $ virtualenv -p /usr/bin/python3.6 venv $ source venv/bin/activate $ pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html $ pip install -r requirements.txt Download BERT Model Checkpoints Before running the repo […]

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Video

Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks This repository holds the source code, pretrained models, and pre-extracted features for the TSP method. Please cite this work if you find TSP useful for your research. @article{alwassel2020tsp, title={TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks}, author={Alwassel, Humam and Giancola, Silvio and Ghanem, Bernard}, journal={arXiv preprint arXiv:2011.11479}, year={2020} } We provide pre-extracted features for ActivityNet v1.3 and THUMOS14 videos. The feature files are saved in H5 format, where we […]

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Color

Automatic color transfer across images in python

color-matcher color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, paintings and film sequences as well as light-field and stopmotion corrections. The methods behind the mappings are based on the approach from Reinhard et al., the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram matching. As shown below our HM-MVGD-HM compound outperforms existing methods. Installation via pip: […]

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