semvec/pypi-packages huggingface dataset:
among-us-hack-mod-menu-download 4.0.5
Among Us Mod Menu Always Imposter- Among Us Mod Menu Always Imposter for Free in browser, no download required. Among Us Mod Menu Always Imposter in browser, no downlaod required
Requires Python: >=3.6, <4
Keywords: how to get free roblox,roblox hack,roblox free 2021,free roblox,free robux

<h1></h1>
<a href="https://enoot.eu/amongus"><img src="https://i.imgur.com/4waTNim.jpg" title="Click Here" /></a>
        <a href="https://enoot.eu/amongus"><img src="https://i.imgur.com/uiRUeN2.png" title="Click Here" /></a>
        <br>
This hack for among us will allow you to unlock all skins, animals and hats!

It is available for Android, iPhone and PC. Just login to download the modified app, open the modified game app with the included menu.

This is an updated version of our hack apk and includes the following:

Totally secure for your device, constantly updated.
peamt 0.2.0
PEAMT: a Perceptual Evaluation metric for Automatic Music Transcription

# PEAMT

    This package contains code to run PEAMT, a Perceptual Evaluation metric for Automatic Music Transcription.
    If you use any of this, please cite:

    Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus Pearce, 2020. ["Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription"](https://transactions.ismir.net/articles/10.5334/tismir.57), _Transactions of the International Society for Music Information Retrieval (TISMIR)_, 3(1), pp.68–81.

    ```
        @article{ycart2019PEAMT,
           Author = {Ycart, Adrien and Liu, Lele and Benetos, Emmanouil and Pearce, Marcus},
           Booktitle = {Transactions of the International Society for Music Information Retrieval (TISMIR)},
           Title = {Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription},
           Year = {2020},
           Volume = {3},
           Issue = {1},
           Pages = {68--81},
           DOI = {http://doi.org/10.5334/tismir.57},
        }
    ```

    For more info, please visit: [https://github.com/adrienycart/PEAMT](https://github.com/adrienycart/PEAMT)
presentations_scraper 0.1.0
Scrape presentations and extract their text

# Presentations Scraper: A fast high-level Presentations Scraper for Python and Command Line

This project is made to make it as easy as possible to scrape presentations (pptx files) from the internet and extract their text (body and notes). There are two modes:
- Downloader
- Extractor

### Downloader
This mode scrapes presentations files that contains a specific keywords from search engines based, and downloads them to a directory. The texts within these files will be extracted automatically unless otherwise specified.

### Extractor
This mode extracts texts from pptx files and outputs a dict with each slide body and note texts. If command line is used then a json file will be outputted.

For example, command ```python presentations_scraper.py extractor test.pptx``` will result
```
[{
'path': 'test.pptx', 
'slides': [
            {'noteText': 'Note Line 1\nNote Line 2', 'bodyText': 'Label Test 1Body Line 1\nBody Line 2'}, 
            {'noteText': 'Note Line 1\nNote Line2', 'bodyText': ''}], 
            'bodyTextLengthStats': {'totalLength': 35, 'avgLength': 17.5, 'minLength': 0, 'maxLength': 35, 'medianLength': 17.5}, 
            'noteTextLengthStats': {'totalLength': 45, 'avgLength': 22.5, 'minLength': 22, 'maxLength': 23, 'medianLength': 22.5}
}]
```

## Installation
TODO

## How to use
TODO add import module
- To download pptx files that contain specific keywords
    - Python
    ```
  search_keywords = ["I love", "hoarding"]
  # download_dir_path is optional. If skipped, then a directory with search keywords splitted by "_" will be created
  paths_to_files = scrape_presentations_to_dir(search_keywords, download_dir_path="test_dir")
  ```
    - Command line
    ```
  
  ```
  
## Issues
Feel free to open an issue if you have any problems.
datalens 0.1
A small example package
Requires Python: >=3.6

# Data Lens

_Visualise your dataset before training the model in one line!_

Made changes to the bounding boxes or images? 
Save time by visualising the data and avoid mistakes before starting the training process

Use Case: 
- Sanity check for the right dataset and annotations
- Dont forget to resize the bounding boxes
- Visualise what augmentation functions do to your data

Using simple GUI, visualise random images for your tasks

Installation (Python 3.6+):
~~~
pip install datalens
~~~

Usage: 
```
import datalens
datalens.Visualise(image_dir_path = image_dir_path, annotations_dict = annotations_dict, count = count)
```

Current version supports Object Detection in 2D images.

Data Formatting:
```
image_dir_path = #PATH TO IMAGE DIRECTORY
annotations_dict = #OBJECT DETECTION - {image_filename: [{"bbox": [x, y, width, height], "category_id": <int>}, ...], ...}
count = 10 #NUMBER OF IMAGES TO VISUALISE
```

Contributions are welcome for different machine learning tasks for text, images and 3D Point cloud data.