semvec/pypi-packages huggingface dataset:
udon2 0.1.0
Prepare your UD trees to be served!
Requires Python: >=3.6

![UDon2 logo](https://github.com/dkalpakchi/udon2/raw/master/images/udon2_logo.png "UDon2")
## Prepare your UD trees to be served!
UDon2 is a library for manipulating dependency trees in the Universal Dependencies format. Written in C++ with Python bindings through Boost.Python. Pre-compiled for Python 3.7-3.9 on Linux (both i386 and x86_64), Windows (win32 and win_amd64) and macOS (x86_64 only).

**Note:** Python 3.6 is supported for versions prior to v0.1.0 on Linux only (both i386 and x86_64).

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Build Status](https://travis-ci.com/udon2/udon2.svg?branch=master)](https://travis-ci.com/udon2/udon2)
[![Downloads](https://pepy.tech/badge/udon2)](https://pepy.tech/project/udon2)

## Documentation
Our online documentation is available at https://udon2.github.io/

## Contributing
Please check our [contribution guidelines](https://github.com/udon2/udon2/blob/master/CONTRIBUTING.md).

## Citing
The associated paper is accepted to the Universal Dependencies Workshop 2020 @ COLING 2020. If UDon2 is of any use in your research, please cite the paper.
```
@inproceedings{kalpakchi2020udon2,
  title={UDon2: a library for manipulating Universal Dependencies trees},
  author={Kalpakchi, Dmytro and Boye, Johan},
  booktitle={Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)},
  pages={120--125},
  year={2020}
}
```
test2-fxd 0.1
A package containing methods to compute FXD score and other popular metrics to find the distance between image distributions. This enable building better generative models for niche domains such as Chest X-Rays.
Requires Python: >=3.6

# FXD Score: An Improved Metric for Evaluating Chest X-Ray Generation Methods

Generative models are widely used to augment data to improve machine learning models trained on insufficient data. The quality of generated data plays a significant role in the model’s performance. Evaluating a quality of the generative models used a challenging problem. Many sample-based evaluation metrics like Fréchet Inception Distance (FID) compare the distributions of real and generated images in a latent space of models trained on the ImageNet dataset. Such metrics work well in evaluating quality of images of common entities but generally fail in case of images from niche domains not present in the ImageNet dataset like chest x-rays. In this work we propose the FXD score which computes Fréchet distance over the latent space of the pretrained TorchXrayVision model. We perform a series of carefully designed experiments to test for necessary conditions for a meaningful metric. Through these experiments we show that the proposed metric shows more discriminability robustness to transformations and is more sensitive to, mode dropping, mode collapsing and overfitting compared to metrics based on ImageNet weights. Which also proves the importance of choosing domain specific representations in evaluating the quality of generative models. We believe that the proposed FXD score will enable researchers in building generation models efficiently which will in turn aid in building better models to analyze patients more accurately.
termaths 0.0.4
Un module de maths pour des élèves de terminales en spécialité maths ou en maths expertes.
Requires Python: >=3.6

# Termaths

Termaths est un module qui est prévu pour les élèves de terminale en spécialité maths ou en maths experts.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install foobar
```

## License
[MIT](https://choosealicense.com/licenses/mit/)