semvec/pypi-packages huggingface dataset:
bertserini-on-telegram 1.2
A library, based on PyTorch, that implements bertserini code.
Requires Python: >=3.7

# Bertserini-on-Telegram

![schema.jpeg](schema/schema.jpg)

Bertserini-on-Telegram is a pytorch Bertserini implementation with a small utility to plug it in a Telegram bot.
The models and dataloaders are written in PyTorchLightning, and are usable on their own, or via PyTorchLightning CLI.

## Project Structure

### A typical top-level directory layout

    .
    ├── bertserini_on_telegram      # Actual project code
    |    ├── data                   # contains the DataModules to handle SQuAD V1.1
    |    ├── models                 # holds the Modules to run validation and inference with BERT
    |    └── utils                  # general helper functions
    ├── examples                    # Example scripts of how to run the code
    ├── LICENSE
    └── README.md


## Installation

Install via [pip](https://pypi.org/project/bertserini-on-telegram/)

```bash
pip install bertserini-on-telegram
```

## Usage

The fastest way to learn how to use this package is by heading on tothe examples folder, and have a look at the python scripts saved in there. In each example folder, there is a config.yaml file holding the run parameters, a script.py file where the actual example code is, and a runnable script.sh file that shows how to run a PytorchLightningCli script with a config file.

To run an example, simply
```bash
cd examples/<example_dir>
./example_shell_script.sh
```


## Running it on Telegram

The telegram example supposes that the used knows how to create a Telegram bot in the first place. Telegram has an awesome tutorial on how to do it, you can find it [here](https://core.telegram.org/bots#3-how-do-i-create-a-bot). 

Once the bot is online, the next step is to insert the bot's private token into the config.yaml file, as shown below (the file needs to be created)

#### **`./example/telegram_bot/telegram_token_id.yaml`**
```yaml
token_id: <token_id>
```


> Be careful when adding a bot to a group, it must have the necessary permission to be able to read the messages.

Now just run the example as shown in the snippet below, and you are good to go :)
```bash
cd examples/telegram_bot
./run_bertbot.sh
```
mdformat_footnote 0.1.1
An mdformat plugin for parsing/validating footnotes
Requires Python: >=3.7
Keywords: mdformat,markdown,markdown-it

# mdformat-footnote

[![Build Status][ci-badge]][ci-link]
[![codecov.io][cov-badge]][cov-link]
[![PyPI version][pypi-badge]][pypi-link]

Footnote format addition for [mdformat](https://github.com/executablebooks/mdformat).

## Development

This package utilises [flit](https://flit.readthedocs.io) as the build engine, and [tox](https://tox.readthedocs.io) for test automation.

To install these development dependencies:

```bash
pip install tox
```

To run the tests:

```bash
tox
```

and with test coverage:

```bash
tox -e py39-cov
```

The easiest way to write tests, is to edit tests/fixtures.md

To run the code formatting and style checks:

```bash
tox -e py39-pre-commit
```

or directly

```bash
pip install pre-commit
pre-commit run --all
```

To run the pre-commit hook test:

```bash
tox -e py39-hook
```

## Publish to PyPi

Either use flit directly:

```bash
pip install flit
flit publish
```

or trigger the GitHub Action job, by creating a release with a tag equal to the version, e.g. `v0.0.1`.

Note, this requires generating an API key on PyPi and adding it to the repository `Settings/Secrets`, under the name `PYPI_KEY`.

[ci-badge]: https://github.com/gaige/mdformat-footnote/workflows/CI/badge.svg?branch=master
[ci-link]: https://github.com/gaige/mdformat-footnote/actions?query=workflow%3ACI+branch%3Amaster+event%3Apush
[cov-badge]: https://codecov.io/gh/gaige/mdformat-footnote/branch/master/graph/badge.svg
[cov-link]: https://codecov.io/gh/gaige/mdformat-footnote
[pypi-badge]: https://img.shields.io/pypi/v/mdformat-footnote.svg
[pypi-link]: https://pypi.org/project/mdformat-footnote
mavis-temp 2.0.0
Config validation for running MAVIS via Snakemake
Requires Python: >=3.6

# MAVIS Config

[![build](https://github.com/bcgsc/mavis_config/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/bcgsc/mavis_config/actions/workflows/build.yml) [![codecov](https://codecov.io/gh/bcgsc/mavis_config/branch/master/graph/badge.svg)](https://codecov.io/gh/bcgsc/mavis_config) ![PyPi](https://img.shields.io/pypi/v/mavis_config.svg)

Workflow configuration validation for [MAVIS](https://github.com/bcgsc/mavis) via snakemake. This package should not be used without [MAVIS](https://github.com/bcgsc/mavis). If you are a [MAVIS](https://github.com/bcgsc/mavis) user, please see [MAVIS](https://github.com/bcgsc/mavis) instead.

## For Developers

This is a separate package from [MAVIS](https://github.com/bcgsc/mavis)
itself so that [MAVIS](https://github.com/bcgsc/mavis) users running snakemake with the
docker container do not need to install [MAVIS](https://github.com/bcgsc/mavis) to check their snakemake config JSON.
This is to ensure that all the hard-to-install [MAVIS](https://github.com/bcgsc/mavis) dependencies are not required to run 
snakemake with docker.