semvec/pypi-packages huggingface dataset:
jupyter-flex 0.9.1
Build dashboards using Jupyter Notebooks
Requires Python: >=3.8
Keywords: dashboards,jupyter,jupyterlab,notebooks,voila

<p align="center">
    <img src="https://raw.githubusercontent.com/danielfrg/jupyter-flex/main/docs/assets/img/logo.png" width="450px">
</p>

<p align="center">
    <a href="https://pypi.org/project/jupyter-flex/">
        <img src="https://img.shields.io/pypi/v/jupyter-flex.svg">
    </a>
    <a href="https://pypi.org/project/mkdocs-jupyter">
        <img src="https://img.shields.io/pypi/pyversions/jupyter-flex.svg">
    </a>
    <a href="https://github.com/danielfrg/jupyter-flex/actions/workflows/test.yml">
        <img src="https://github.com/danielfrg/jupyter-flex/workflows/test/badge.svg">
    </a>
    <a href="https://github.com/danielfrg/jupyter-flex/actions/workflows/docs.yml">
        <img src="https://github.com/danielfrg/jupyter-flex/workflows/docs/badge.svg">
    </a>
    <a href="https://codecov.io/gh/danielfrg/jupyter-flex?branch=main">
        <img src="https://codecov.io/gh/danielfrg/jupyter-flex/branch/main/graph/badge.svg">
    </a>
    <a href="https://github.com/danielfrg/jupyter-flex/blob/main/LICENSE.txt">
        <img src="https://img.shields.io/:license-Apache%202-blue.svg">
    </a>
    <a href="https://mybinder.org/v2/gh/danielfrg/jupyter-flex/main?urlpath=voila%2Ftree%2Fexamples">
        <img src="https://mybinder.org/badge_logo.svg">
    </a>
</p>

<p align="center">
  <a href="https://jupyter-flex.danielfrg.com">Documentation</a>
  ·
  <a href="#installation">Installation</a>
</p>

# Build dashboards using Jupyter Notebooks

- Use Markdown headers and Jupyter Notebook cell tags to define the dashboard layout and its components
- Flexible and easy way to specify row and column based layouts
- Use [nbconvert](https://nbconvert.readthedocs.io/en/latest/) to create static reports
- Use [Voila](https://github.com/voila-dashboards/voila) to start a live Jupyter Kernel for fully dynamic applications
- Support for [Jupyter widgets](https://ipywidgets.readthedocs.io/en/latest/)

<a href="https://mybinder.org/v2/gh/danielfrg/jupyter-flex/main?urlpath=%2Fvoila%2Frender%2Fexamples%2Fmovie-explorer.ipynb"><img src="https://jupyter-flex.danielfrg.com/assets/img/screenshots/jupyter_flex.tests.test_examples/apps_movie-explorer-reference.png" alt="Jupyter-flex: Movie Explorer"  width=270></a>
<a href="https://jupyter-flex.danielfrg.com/examples/nba-scoring.html"><img src="https://jupyter-flex.danielfrg.com/assets/img/screenshots/jupyter_flex.tests.test_examples/apps_nba-scoring-reference.png" alt="Jupyter-flex: NBA Scoring" width=270></a>
<a href="https://jupyter-flex.danielfrg.com/examples/altair.html"><img src="https://jupyter-flex.danielfrg.com/assets/img/screenshots/jupyter_flex.tests.test_examples/plots_altair-reference.png" alt="Jupyter-flex: Bokeh plots"  width=270></a>

## Installation

```shell
pip install jupyter-flex
```

## Documentation

Learn more in the [jupyter-flex documentation](https://jupyter-flex.danielfrg.com).

## Contributing

See [CONTRIBUTING.md](https://github.com/danielfrg/jupyter-flex/blob/main/CONTRIBUTING.md).
torch-btg 0.0.1
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers
Requires Python: >=3.6

# Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

A PyTorch implementation of Bridging the Gap (BtG) losses including F-beta (F1), Accuracy, and AUROC.

Project Webpage: [btg.yale.edu](https://btg.yale.edu)

[PDF Paper](https://btg.yale.edu/papers/Bridging_the_Gap_Unifying_the_Training_and_Evaluation_of_Neural_Network_Binary_Classifiers.pdf)

Citation:

```
@inproceedings{tsoi2022bridging,
  title         = {Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers},
  author        = {Tsoi, Nathan and Candon, Kate and Li, Deyuan and Milkessa, Yofti and V{\'a}zquez, Marynel},
  booktitle     = {Advances in Neural Information Processing Systems},
  year          = {2022}
}
```

## Usage

Install the `torch-btg` package:

```
pip install torch-btg
```

Use the desired loss in your code, for example,

- `F1-loss`:
```
from torch_btg.loss import f1_loss
...
criterion = fb_loss(beta=1.0)
...
```

- `Accuracy loss`:
```
from torch_btg.loss import accuracy_loss
...
criterion = accuracy_loss()
...
```

## Development

### Setup

```
python -m pip install --user tox
```

Then run tests with:

```
tox
```
telebit-python 0.1.0
A wrapper around telebit (ngrok like service).
Keywords: python,python3,telebit,tunnel


### README.md 👋
# telebit-python
[![Awesome Badges](https://img.shields.io/badge/badges-awesome-green.svg)](https://github.com/Xeroxxhah/6u4rd)
[![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](https://opensource.org/licenses/)
![Contribution](https://img.shields.io/badge/Contributions-Welcome-<brightgreen>)
[![Active](http://img.shields.io/badge/Status-Active-green.svg)](https://github.com/Xeroxxhah)
[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)
[![releases](https://img.shields.io/github/release/Xeroxxhah/6u4rd.svg)](https://github.com/Xeroxxhah/6u4rd/releases)

A wrapper around [telebit](https://telebit.cloud/) (A ngrok like service).

# Installation & Usage:


# Follow these steps: 
- 1st: ```git clone https://github.com/Xeroxxhah/telebit-python```
- 2nd: ```cd telebit-python```
- 3rd: ```sudo python3 setup.py install```


### Bug report
Found any bug!
Report it to me at xeroxxhah@pm.me
or open an [issue](https://github.com/Xeroxxhah/telebit-python/issues)

### TODO:
Add more features.

### Contributions:
All contributions are welcomed.fork this repo,improve it and [pull requests](https://github.com/Xeroxxhah/telebit-python/pulls)
### License
Distributed under GPLV3.0