semvec/pypi-packages huggingface dataset:
bavera 2.0.0
A Python wrapper for the Discord API
License: MIT
Requires Python: >=3.8.0

Bavera
==========

.. image:: https://discord.com/api/guilds/908591684428918785/embed.png
   :target: https://discord.gg/8vrHAqWgYR
   :alt: Discord server invite
.. image:: https://img.shields.io/pypi/v/bavera.svg
   :target: https://pypi.python.org/pypi/bavera
   :alt: PyPI version info
.. image:: https://img.shields.io/pypi/pyversions/bavera.svg
   :target: https://pypi.python.org/pypi/bavera
   :alt: PyPI supported Python versions

A modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python.

Key Features
-------------

- Modern Pythonic API using ``async`` and ``await``.
- Proper rate limit handling.
- Optimised in both speed and memory.

Installing
----------

**Python 3.8 or higher is required**

To install the library without full voice support, you can just run the following command:

.. code:: sh

    # Linux/macOS
    python3 -m pip install -U bavera

    # Windows
    py -3 -m pip install -U bavera

Otherwise to get voice support you should run the following command:

.. code:: sh

    # Linux/macOS
    python3 -m pip install -U "bavera[voice]"

    # Windows
    py -3 -m pip install -U bavera[voice]

To install the speed version with additional speedups run the following command:

.. code:: sh

    # Linux/MacOS
    python3 -m pip install -U bavera[speed]

    # Windows
    py -3 -m pip install -U bavera[speed]

To install the development version, do the following:

.. code:: sh

    $ git clone https://github.com/bavera/bavera
    $ cd bavera
    $ python3 -m pip install -U .[voice]


Optional Packages
~~~~~~~~~~~~~~~~~~

* `PyNaCl <https://pypi.org/project/PyNaCl/>`__ (for voice support)

Please note that on Linux installing voice you must install the following packages via your favourite package manager (e.g. ``apt``, ``dnf``, etc) before running the above commands:

* libffi-dev (or ``libffi-devel`` on some systems)
* python-dev (e.g. ``python3.8-dev`` for Python 3.8)

Quick Example
--------------

.. code:: py

    import bavera

    class MyClient(bavera.Plugin):
        async def on_ready(self):
            print('Logged on as', self.user)

        async def on_message(self, message):
            # don't respond to ourselves
            if message.author == self.user:
                return

            if message.content == 'ping':
                await message.channel.send('pong')

    client = MyClient()
    client.run('token')

Bot Example
~~~~~~~~~~~~~

.. code:: py

    import bavera
    import plugins
    from plugins import commands

    Plugger = commands.Plugger(command_prefix='>')

    @Plugger.command()
    async def ping(ctx):
        await ctx.send('pong')

    Plugger.run('token')

You can find more examples in the examples directory.

Links
------

- `Documentation <https://bavera.readthedocs.io/en/latest/index.html>`_
- `Official Discord Server <https://discord.gg/8vrHAqWgYR>`_
- `Discord API <https://discord.gg/discord-api>`_
GCProc 0.1.0
Python Implementation of the R-based GCProc
License: MIT
Requires Python: >=3.6


# GCPROCPY

This is a Python implementation based on `https://github.com/AskExplain/gcproc/blob/1_prepare`


To be done:
* Using a zero-shot NN to provide good intialization.
* Extend the technique to multi-linear agebra. This is of interest in biological structures data where there is metadata that can be used.
* generalized assigment problem.
* Impuation learning
* Kernelized version.
humilis-firehose 0.3.0
Humilis plug-in to create Firehose delivery streams
License: MIT

Humilis plug-in to deploy Firehose delivery streams
===================================================

[![PyPI](https://img.shields.io/pypi/v/humilis-firehose.svg?style=flat)](https://pypi.python.org/pypi/humilis-firehose)
[![Build Status](https://travis-ci.org/humilis/humilis-firehose.svg?branch=master)](https://travis-ci.org/humilis/humilis-firehose)

A [humilis][humilis] plug-in layer that deploys
[Firehose delivery streams][firehose].

[firehose]: http://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
[humilis]: https://github.com/InnovativeTravel/humilis
[redshift]: https://aws.amazon.com/documentation/redshift/
[s3]: https://aws.amazon.com/documentation/s3/


## Installation


```
pip install humilis-firehose
```


To install the development version:

```
pip install git+https://github.com/humilis/humilis-firehose
```


## Development

Assuming you have [virtualenv][venv] installed:

[venv]: https://virtualenv.readthedocs.org/en/latest/

```
make develop
```

Configure humilis:

```
make configure
```


## Testing

There is no logic in this layer beyond the deployment of AWS resources so
there is no unit test suite. You can test the deployment of the Firehose
delivery streams with:

```bash
make create
```

Then you can run integration tests using:

```
make test
```

Don't forget to delete the deployment after you are done:

```bash
make delete
```


## More information

See [humilis][humilis] documentation.

[humilis]: https://github.com//humilis/blob/master/README.md


## Contact

If you have questions, bug reports, suggestions, etc. please create an issue on
the [GitHub project page][github].

[github]: http://github.com/humilis/humilis-firehose


## License

This software is licensed under the [MIT license][mit].

[mit]: http://en.wikipedia.org/wiki/MIT_License

See [License file][LICENSE].

[LICENSE]: https://github.com/humilis/humilis-firehose/blob/master/LICENSE.txt


© 2016 German Gomez-Herrero, [Find Hotel][fh] and others.

[fh]: http://company.findhotel.net
m3tl 0.7.0
BERT for Multi-task Learning
License: MIT
Requires Python: >=3.5.0

# M3TL



**M**ulti-**M**odal **M**ulti-**T**ask **L**earning

## Install

```
MASKED
```

## What is it

This is a project that uses transformers(based on huggingface transformers) as base model to do **multi-modal multi-task learning**.   

## Why do I need this

Multi-task learning(MTL) is gaining more and more attention, especially in deep learning era. It is widely used in NLP, CV, recommendation, etc. However, MTL usually involves complicated data preprocessing, task managing and task interaction. Other open-source projects, like TencentNLP and PyText, supports MTL but in a naive way and it's not straightforward to implement complicated MTL algorithm. In this project, we try to make writing MTL model as easy as single task learning model and further extend MTL to multi-modal multi-task learning. To do so, we expose following MTL related programable module to user:

- problem sampling strategy
- loss combination strategy
- gradient surgery
- model after base model(transformers)

Apart from programable modules, we also provide various built-in SOTA MTL algorithms.

In a word, you can use this project to:

- implement complicated MTL algorithm
- do SOTA MTL without diving into details
- do multi-modal learning

And since we use transformers as base model, you get all the benefits that you can get from transformers!

## What type of problems are supported?

```
params = Params()
for problem_type in params.list_available_problem_types():
    print('`{problem_type}`: {desc}'.format(
        desc=params.problem_type_desc[problem_type], problem_type=problem_type))

```

    `cls`: Classification
    `multi_cls`: Multi-Label Classification
    `seq_tag`: Sequence Labeling
    `masklm`: Masked Language Model
    `pretrain`: NSP+MLM(Deprecated)
    `regression`: Regression
    `vector_fit`: Vector Fitting
    `premask_mlm`: Pre-masked Masked Language Model
    `contrastive_learning`: Contrastive Learning



## Get Started

Please see tutorials.