semvec/pypi-packages huggingface dataset:
easybird 0.0.3
A python toolkit for Bird Activity Detection (BAD)
Requires Python: >=3.6

# easybird
![PyPI](https://img.shields.io/pypi/v/easybird?color=df&style=flat)&nbsp;
![GitHub](https://img.shields.io/github/license/realzza/easybird?color=%23FFB6C1&style=flat)&nbsp;
![GitHub last commit](https://img.shields.io/github/last-commit/realzza/easybird?color=orange&style=flat)&nbsp;
![GitHub top language](https://img.shields.io/github/languages/top/realzza/easybird?color=%236495ed&style=flat)&nbsp;
[![CodeFactor](https://www.codefactor.io/repository/github/realzza/easybird/badge)](https://www.codefactor.io/repository/github/realzza/easybird)&nbsp;

---

**Easybird** is python toolkit for Bird Activity Detection (BAD).

## Setup and Install
We recommend using **conda** for environment management, since we use conda-forge to install an essential library `libsndfile`. To setup, copy the following command to your terminal.
```bash
conda create -n bird
conda activate bird
conda install -c conda-forge libsndfile -y
```

After setup the virtual env, install with pip.
```bash
pip install easybird
```

## Single Wav
Identify bird activities for single waveform.
```python
from easybird import detection

hasBird, confidence = detection.from_wav('bird.wav')
```
Output
```python
print(hasBird)
>>> True
print(confidence)
>>> 0.9996312260627747
```

## Multiple Wavs
Identify bird activities for multiple wavforms.
```python
from easybird import detection

results = detection.from_wavs(['bird1.wav','bird2.wav','bird3.wav'])
```
Output
```python
print(results)
>>> [(bird1, True, 0.99963122), (bird2, True, 0.37834975), (bird3, True, 0.87340939)]
```
hack-biancheng-shili-jingjiang2 2022.4.30.0
Hack编程实例精讲II
Requires Python: >=3.6

# Hack编程实例精讲II

## 下载

### Docker

```
docker pull apachecn0/hack-biancheng-shili-jingjiang2
docker run -tid -p <port>:80 apachecn0/hack-biancheng-shili-jingjiang2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install hack-biancheng-shili-jingjiang2
hack-biancheng-shili-jingjiang2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g hack-biancheng-shili-jingjiang2
hack-biancheng-shili-jingjiang2 <port>
# 访问 http://localhost:{port} 查看文档
```
MultiEncoder 0.0.6
MultiEncoder
Requires Python: >=3.7

## SqueezeFormer
#### Get layer embeddings from a RoBERTa, Longformer or BigBird
###### built on top of Huggingface and Pytorch
###### OBS: bigbird not tested yet



### Why is this useful?
* Increase Dataset Size by 8-12 x
* Treat them as Features
* SqueezeFormer is a fast and easy way to get embeddings from a pretrained model

#### Install this 
``!pip install MultiEncoder``

#### How to Import?
````python

from mle import multi_layer_encoder
    
````

### How to use?
````python

# intantiaite model
le = multi_layer_encoder.multi_layer_encoder("allenai/longformer-base-4096")
    
# encode return list of embeddings in numpy. [its mean pooling by default]
list_of_encoded_inputs, dect = le.multi_encode("Hi this is a dami text muhahaha")
    
# dect is a dictionary of layers and non pooled embeddings. This is done to give full freedom to developers!

    
#last item in the list is the last hidden state(embeddings) output (mean pooled)
print(len(list_of_encoded_inputs)) #7
print(list_of_encoded_inputs[-1].shape) #after mean pooling: (768,)
    
````

#### increase/decrease the layers 
This will return the starting layers to save from until the last layer (which is 13).
````python
 #default is 6 but you can increase to 8 
le.multi_encode(input_text, encode_layers=6)
    
#you can even change to maxpool instead of mean pool
le.multi_encode(input_text, encode_layers=6, max_pool=True)
````
nohack-2008-part4 2022.4.30.0
非安全黑客手册合订本2008年③④季第二部分
Requires Python: >=3.6

# 非安全黑客手册合订本2008年③④季第二部分

## 下载

### Docker

```
docker pull apachecn0/nohack-2008-part4
docker run -tid -p <port>:80 apachecn0/nohack-2008-part4
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install nohack-2008-part4
nohack-2008-part4 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g nohack-erlinglingba-part4
nohack-erlinglingba-part4 <port>
# 访问 http://localhost:{port} 查看文档
```
UzTransliteration 0.0.6
UzTransliteration | Transliteration tool for Uzbek language - Cyrillic<>Latin<>NewLatin
Requires Python: >=3.6
Keywords: python,transliteration,uzbek,cyrillic,latin

<div id="top"></div>

<!-- PROJECT SHIELDS -->

<!-- PROJECT LOGO -->
<br />
<div align="center">
  <h3 align="center">UzTranslit | State-of-the-art machine transliteration tool for Uzbek language, Cyrillic<>Latin<>NewLatin</h3>
  <p align="center">
    The main goal of this paper is to present a state-of-the-art machine transliteration tool between three common scripts used in low-resource Uzbek language: old Cyrillic, currently official Latin, and newly announced New-Latin alphabets, which was created using a combination of rule-based and statistical approaches. The created tool is available as an open-source Python package, as well as a web-based application including a public API
  </p>
</div>



<!-- ABOUT THE PROJECT -->
## About The Project
<div align="center">
<img src="https://github.com/elmurod1202/UzTranslit/blob/master/src/web-uinterface.png?raw=true" width = "600" Alt = "Web-interface of the tool">
</div>


Feel free to use the tool presented in this project, and if you find it useful, plese make sure to cite the paper [here](...) (coming soon...)
Demo of the web-based transliteration tool can be seen [here](https://nlp.urdu.uz/?menu=translit).


In this paper, we presented a Python code, a web tool, and an API created for the Uzbek language that performs machine transliteration between two popularly used Cyrillic and Latin alphabets, as well as a newly reformed version of the Latin alphabet, which, according to the governmental decree, all legal texts will have been completely adapted to by year 2023.

<p align="right">(<a href="#top">back to top</a>)</p>



### Built With

Programming language used:

* [Python](https://www.python.org/)

These are the major libraries used inside Python:

* [scikit-learn : A set of python modules for machine learning](https://scikit-learn.org/stable/)


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<!-- LICENSE -->
## License

Distributed under the MIT LICENSE. See `LICENSE.txt` for more information.

<p align="right">(<a href="#top">back to top</a>)</p>


<!-- ACKNOWLEDGMENTS -->
## Acknowledgments

We are grateful for these resources and tutorials for making this repository possible:

* [GitHub Readme template](https://github.com/othneildrew/Best-README-Template)
* [Visual Studio Code](https://code.visualstudio.com/)


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