semvec/pypi-packages huggingface dataset:
english-detector 0.1.0
A python package to detect english text
Requires Python: >=3.10.4

# English Detector

[![Downloads](https://static.pepy.tech/personalized-badge/english-detector?period=total&units=none&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/english-detector)

A python package to detect english text.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Support](#support)
- [Contributing](#contributing)

## Installation

Install the package using pip:

```sh
pip install english-detector
```

## Usage

```python
from english-detector import detect_english

text = '''Hansel and Gretel are a brother and sister abandoned in a forest, where they fall into the hands of a witch who lives in a house made of gingerbread, cake, and candy. The cannibalistic witch intends to fatten Hansel before eventually eating him, but Gretel pushes the witch into her own oven and kills her.'''

print(detect_english(text)) # prints True
```

## Support

Please [open an issue](https://github.com/apinanyogaratnam/english-detector/issues/new) for support.

## Contributing

Please contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/apinanyogaratnam/english-detector/compare/).
textsimi 0.4.2
A package for text similarity

# Text-Similarity
Package for texts similarity, based on [textdistance](https://pypi.org/project/textdistance/)

# Usage
1. installation
  ```
  pip install textsimi
  ```
2. example usage:
  ```python
  from testsimi import textsimilarity as ts
  ts.print_all_algorithms()

  print(ts.compute_similarity('hello','hi'))

  print(ts.top_K_similarity_between_one_mention_and_many_candidates(5,'hi',['hi','hihi','hello',"what's up", 'greetings', 'how are you', 'hallo', 'wie gehts']))
  
  
  '''
  ['hamming', 'mlipns', 'levenshtein', 'damerau_levenshtein', 'jaro_winkler', 'strcmp95', 'needleman_wunsch', 'gotoh', 'smith_waterman', 'jaccard', 'sorensen', 'tversky', 'overlap', 'tanimoto', 'cosine', 'monge_elkan', 'bag', 'ratcliff_obershelp', 'arith_ncd', 'rle_ncd', 'bwtrle_ncd', 'sqrt_ncd', 'entropy_ncd', 'bz2_ncd',   'zlib_ncd', 'editex', 'prefix', 'postfix', 'length', 'identity', 'matrix']
  Current algorithm is Jaccard({'qval': 1, 'as_set': False, 'external': True})
  0.16666666666666666
  ['hi', 'hihi', 'wie gehts', 'hallo', 'hello']
  '''
  ```
3. [colab notebook for tf-idf text similarity](https://colab.research.google.com/gist/leoxiang66/1275ad6c11e97df29abe474c50712668/tf-idf-text-similarity.ipynb)
mndynamics 0.1.0
Modeling neural dynamcs
Requires Python: >=3.1

### Modeling Neural Dynamics
-  An Introduction to Modeling Neuronal Dynamics - Christoph Borgers in python

### installation

<!-- #### from pip
```sh
pip3 install mndynamics
``` -->

#### from source code
For runnig the notebooks online, there is no need for installation and just 
use your browser, but you can intall the package locally from source :
```sh
pip3 install -e .
```


![Cover](https://github.com/Ziaeemehr/mndynamics/blob/main/Screenshot%202022-12-20%20at%2023.24.36.png )
<p align="center">
 myBinder interface to run the notebooks online. 
</p>

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Ziaeemehr/mndynamics/main?labpath=mndynamics%2Fexamples%2FIntro.ipynb)