Network

A simple URL shortener built with Flask

URL Shortener A simple URL shortener built with Flask and MongoDB. Getting Started With Docker Prerequisites Running From your terminal/command prompt run: docker-compose up Then point your browser to http://127.0.0.1:5000/. Without Docker Installing Requirements (Optional) Install virtualenv andvirtualenvwrapper and create a new environment. Run pip install -r requirements.txt. Setting up MongoDB You can either: Install MongoDB locally by going here. or: Configuration The following properties can be configured: Name Purpose Default DEBUG If debug mode is enabled. False ENV Environment […]

Read more
Tool

Build custom OSINT tools and APIs with python

osint Build custom OSINT tools and APIs with this python package! It includes different OSINT modules for performing reconnaissance on the target, and a built-in database for mapping and visuialzing some of the reconnaissance results. The final results is a json output that can be intergrated with other projects Install pip3 install osint Usage Example – Scan ips or domains for http and https #Remember you need higher privileges from osint import QBDns, QBScan targets = QBDns().convert_to_ips([“http://test.com”,”1.2.3.4″] ) targets = […]

Read more
Tool

A Python port and library-fication of the midicsv tool

py_midicsv A Python library inspired by the midicsv tool created by John Walker. Its main purpose is to bidirectionally convert between the binary MIDI format and a human-readable interpretation of the contained data in text format, expressed as CSV. If you found this library, you probably already know why you need it. Installation py_midicsv can be installed via pip: $ pip install py_midicsv Alternatively you can build the package by cloning this repository and installing via poetry: $ git clone […]

Read more
Python

Python’s map() Function: Transforming Iterables

Python’s map() is a built-in function that allows you to process and transform all the items in an iterable without using an explicit for loop, a technique commonly known as mapping. map() is useful when you need to apply a transformation function to each item in an iterable and transform them into a new iterable. map() is one of the tools that support a functional programming style in Python. In this course, you’ll learn: How Python’s map() works How to […]

Read more
Advanced, NLP, Project, Technique

Building a Conversational Bot using LUIS

Introduction Companies are increasingly inclining towards having chatbots for their businesses for multiple applications. Amongst the numerous API providers in the chatbot landscape that focus on Natural Language Programming (NLP) and Natural Language Understanding (NLU), I would be demonstrating how to build a chatbot that can automate the process of scheduling interviews using Microsoft’s LUIS. Scheduling interviews comes with a lot of challenges like finding out a suitable slot for everyone, including other participants, rescheduling an interview on a participant’s […]

Read more
Machine Learning

PyTorch Implementation of Region Similarity Representation Learning

ReSim This repository provides the PyTorch implementation of Region Similarity Representation Learning (ReSim) described in this paper: @Article{xiao2021region, author = {Tete Xiao and Colorado J Reed and Xiaolong Wang and Kurt Keutzer and Trevor Darrell}, title = {Region Similarity Representation Learning}, journal = {arXiv preprint arXiv:2103.12902}, year = {2021}, } tldr; ReSim maintains spatial relationships in the convolutional feature maps when performing instance contrastive pre-training, which is useful for region-related tasks such as object detection, segmentation, and dense pose estimation. […]

Read more
Machine Learning

A Python 3 package providing several time-efficient algorithms

AntroPy AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals. Installation pip install antropy Functions Entropy import numpy as np import antropy as ant np.random.seed(1234567) x = np.random.normal(size=3000) # Permutation entropy print(ant.perm_entropy(x, normalize=True)) # Spectral entropy print(ant.spectral_entropy(x, sf=100, method=’welch’, normalize=True)) # Singular value decomposition entropy print(ant.svd_entropy(x, normalize=True)) # Approximate entropy print(ant.app_entropy(x)) # Sample entropy print(ant.sample_entropy(x)) # Hjorth mobility and complexity print(ant.hjorth_params(x)) […]

Read more
Text

A python interactive visualization tool for text summarization

SummVis Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization SummVis is an open-source visualization tool that supports fine-grained analysis of summarization models, data, and evaluation metrics. Through its lexical and semantic visualizations, SummVis enables in-depth exploration across important dimensions such as factual consistency and abstractiveness. Authors: Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema RajaniSalesforce Research Stanford Hazy Research User guide Overview SummVis is a tool for analyzing abstractive summarization systems. It provides fine-grained insights on summarizationmodels, […]

Read more
Excel

Simple XLSX and CSV to dictionary converter with python

sheet2dict A simple XLSX/CSV reader – to dictionary converter Installing To install the package from pip, first run: python3 -m pip install –no-cache-dir sheet2dict Required pip packages for sheet2doc: csv, openpyxl Usage This library has 2 main features: reading a spreadsheet files and converting them to array of python dictionaries. – XLSX Use xlsx_to_dict() method when converting form spreadsheets.Supported file formats for spreadsheets are: .xlsx,.xlsm,.xltx,.xltm # Import the library from sheet2dict import Worksheet # Create an object ws = Worksheet() […]

Read more
Machine Learning

Flexible interface for high performance research using SOTA Transformers

lightning-transformers Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra. Installation Option 1: from PyPI pip install lightning-transformers # instead of: `python train.py …`, run with: pl-transformers-train … Option 2: from source git clone https://github.com/PyTorchLightning/lightning-transformers.git cd lightning-transformers pip install . python train.py … # the `pl-transformers-train` endpoint is also available! Quick recipes Train bert-base-cased on the CARER emotion dataset using the Text Classification task. python train.py task=nlp/text_classification dataset=nlp/text_classification/emotion See the composed Hydra config used under-the-hood […]

Read more
1 … 660 661 662 663 664 … 928