semvec/pypi-packages huggingface dataset:
mygpio 1.0.1
GPIO Library which extends GPIO functions

# Hello World

This is an example project demonstrating how to publish a python module to PyPI

## Installation

Run the following to install:

```python
pip install mygpio
```

## Usage

```python
from mygpio import set_on

# Switch on pin 5
set_on(5)

```
## Developing Hello World

To install helloworld, along with the tools you need to develop and run tests, run the following in your virtualenv:

```bash
$ pip install -e .[dev]
```
jgreenepack 0.1.0
Swagger Petstore
Keywords: Swagger,Swagger Petstore

# jgreenepack
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)

This Python package is automatically generated by the [Swagger Codegen](https://github.com/swagger-api/swagger-codegen) project:

- API version: 1.0.0
- Package version: 0.1.0
- Build package: io.swagger.codegen.languages.PythonClientCodegen

## Requirements.

Python 2.7 and 3.4+

## Installation & Usage
### pip install

If the python package is hosted on Github, you can install directly from Github

```sh
pip install git+https://github.com//.git
```
(you may need to run `pip` with root permission: `sudo pip install git+https://github.com//.git`)

Then import the package:
```python
import jgreenepack 
```

### Setuptools

Install via [Setuptools](http://pypi.python.org/pypi/setuptools).

```sh
python setup.py install --user
```
(or `sudo python setup.py install` to install the package for all users)

Then import the package:
```python
import jgreenepack
```

## Getting Started

Please follow the [installation procedure](#installation--usage) and then run the following:

```python
from __future__ import print_function
import time
import jgreenepack
from jgreenepack.rest import ApiException
from pprint import pprint

# create an instance of the API class
api_instance = jgreenepack.PetsApi(jgreenepack.ApiClient(configuration))

try:
    # Create a pet
    api_instance.create_pets()
except ApiException as e:
    print("Exception when calling PetsApi->create_pets: %s\n" % e)

```

## Documentation for API Endpoints

All URIs are relative to *http://petstore.swagger.io/v1*

Class | Method | HTTP request | Description
------------ | ------------- | ------------- | -------------
*PetsApi* | [**create_pets**](docs/PetsApi.md#create_pets) | **POST** /pets | Create a pet
*PetsApi* | [**list_pets**](docs/PetsApi.md#list_pets) | **GET** /pets | List all pets
*PetsApi* | [**show_pet_by_id**](docs/PetsApi.md#show_pet_by_id) | **GET** /pets/{petId} | Info for a specific pet


## Documentation For Models

 - [Error](docs/Error.md)
 - [Pet](docs/Pet.md)
 - [Pets](docs/Pets.md)


## Documentation For Authorization

 All endpoints do not require authorization.


## Author
SupCL-Seq 0.0.1
Supervised Contrastive Learning for Downstream Optimized Sequence representations
Requires Python: >=3.6

# SupCL-Seq :book:
Supervised Contrastive Learning for Downstream Optimized Sequence representations (**SupCS-Seq**) extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP. By altering the dropout mask probability in standard Transformer architectures (e.g. *BERT_base*), for every representation (anchor), we generate augmented altered views. A supervised contrastive loss is then utilized to maximize the system’s capability of pulling together similar samples (e.g. anchors and their altered views) and pushing apart the samples belonging to the other classes. Despite its simplicity, SupCL-Seq leads to large gains in many sequence classification tasks on the GLUE benchmark compared to a standard *BERT_base*, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B.

This package can be easily run on almost all of the transformer models in [`Huggingface`](https://huggingface.co/) :hugs: that contain an encoder including but not limited to:

1. [ALBERT](https://huggingface.co/transformers/model_doc/albert.html)
2. [BERT](https://huggingface.co/transformers/model_doc/bert.html)
3. [BigBird](https://huggingface.co/transformers/model_doc/bigbird.html)
4. [RoBerta](https://huggingface.co/transformers/model_doc/roberta.html)
5. And many more!


## Installation
Simply follow:

```bash
$ pip install SupCL-Seq
```

## Usage
The package builds on the [`trainer`](https://huggingface.co/transformers/main_classes/trainer.html) from [`Huggingface`](https://huggingface.co/):hugs:. Therefore, its use is exactly similar to [`trainer`](https://huggingface.co/transformers/main_classes/trainer.html).

```python
from SupCL_Seq import SupCsTrainer

SupCL_trainer = SupCsTrainer.SupCsTrainer(
            w_drop_out=[0.0,0.05,0.2],      # Number of views and their associated mask drop-out probabilities [Optional]
            temperature= 0.05,              # Temeprature for the contrastive loss function [Optional]
            def_drop_out=0.1,               # Default drop out of the transformer, this is usually 0.1 [Optional]
            pooling_strategy='mean',        # Strategy used to extract embeddings can be from `mean` or `pooling` [Optional]
            model = model,                  # model
            args = CL_args,                 # Arguments from `TrainingArguments` [Optional]
            train_dataset=train_dataset,    # Train dataloader
            tokenizer=tokenizer,            # Tokenizer
            compute_metrics=compute_metrics # If you need a customized evaluation [Optional]
        )

```