semvec/pypi-packages huggingface dataset:
onsen 0.1.1
rain sounds straight to your terminal!
License: MIT
Keywords: rain,cli,onsen,rain,focus,water,sounds,white noise

# onsen ♨️
![GitHub](https://img.shields.io/github/license/paulopacitti/onsen?color=%23a3be8c&style=flat-square)
![PyPI](https://img.shields.io/pypi/v/onsen?color=%23bf616a&style=flat-square)

> "Welcome to `onsen`! (いらっしゃいませ)"

![demo gif](https://raw.githubusercontent.com/paulopacitti/onsen/master/screenshots/demo.gif)

- **Water sounds** straight in your **terminal** with this beautiful `cli`!
- Tested in **Linux** 🐧 and **macOS** 🍎
- _Tattoo-friendly_ 👍

## Installation ⬇️
`pip3 install onsen`

## Usage ⌨️
- Type `onsen` **anywhere** in your terminal :sparkles:

## Credits 🎥
- 🐍 **Audio** programming made with [pygame](https://www.pygame.org) (yeah, the game framework) 
- 💄 **Text User Interface (TUI)** made with [rich](https://github.com/willmcgugan/rich)
- 🦄 Demo gif made with [Terminalizer](https://github.com/faressoft/terminalizer) 
- 💡 **Heavily** inspired by [Dr. Ir. S. Pigeon's work with MyNoise](https://mynoise.net/) 

## Contributing 💕
- ⭐ If you like it, leave your **star** in this project 
- 💟 If you would like to complain/suggest/contribute to this project, **feel free to open a issue**
xswem 1.0.0
A simple and explainable deep learning model for NLP.
License: MIT
Requires Python: >=3.6
Keywords: nlp fast machine learning deep simple tensorflow model word embeddings keras glove explainable swem global local explanations

# XSWEM

[![Build Status](https://img.shields.io/travis/KieranLitschel/XSWEM/main.svg?label=main)](https://travis-ci.org/KieranLitschel/XSWEM) [![Build Status](https://img.shields.io/travis/KieranLitschel/XSWEM/develop.svg?label=develop)](https://travis-ci.org/KieranLitschel/XSWEM)

A simple and explainable deep learning model for NLP implemented in TensorFlow.

Based on SWEM-max as proposed by Shen et al. in [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms, 2018](https://arxiv.org/pdf/1805.09843.pdf).

This package is currently in development. The purpose of this package is to make it easy to train and explain SWEM-max. 

You can find demos of the functionality we have implemented in the [notebooks](https://github.com/KieranLitschel/XSWEM/blob/main/notebooks) directory of the package. Each notebook has a badge that allows you to run it yourself in Google Colab. We will add more notebooks as new functionality is added.

For a demo of how to train a basic SWEM-max model see [train_xswem.ipynb](https://github.com/KieranLitschel/XSWEM/blob/main/notebooks/train_xswem.ipynb).

## Local Explanations

We are currently implementing some methods we have developed for local explanations.

### local_explain_most_salient_words

So far we have only implemented the local_explain_most_salient_words method. This method extracts the words the model has learnt as most salient from a given input sentence. Below we show an example of this method using a sample from the [ag_news](https://huggingface.co/datasets/viewer/?dataset=ag_news) dataset. This method is explained in more detail in the [local_explain_most_salient_words.ipynb](https://github.com/KieranLitschel/XSWEM/blob/main/notebooks/local_explain_most_salient_words.ipynb) notebook.

![local_explain_most_salient_words.png](https://github.com/KieranLitschel/XSWEM/blob/main/resources/images/local_explain_most_salient_words.png)

## Global Explanations

We have implemented the global explainability method proposed in section 4.1.1 of the original paper. You can see a demo of this method in the notebook [global_explain_embedding_components.ipynb](https://github.com/KieranLitschel/XSWEM/blob/main/notebooks/global_explain_embedding_components.ipynb).

## How to install

This package is hosted on [PyPI](https://pypi.org/project/xswem/) and can be installed using pip.

```
pip install xswem
```
buseswarsaw 1.0.1
Python package containing tools for analysing data on buses in Warsaw
License: MIT

# buseswarsaw Package

Python package containing tools for analysing data on buses in Warsaw.

This package provides tools for:

- downloading data on locations of buses in Warsaw,

- downloading data on locations of bus stops,

- downloading schedule for a given line and bus stop,

- finding buses that exceeded 50 km/h for the given dataset,

- plotting data on the map,

- computing percentage of buses that exceeded 50 km/h near the given place,

- checking punctuality of buses of given line for given bus stop.