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🎉 Bump to v0.2.0 & first pypi deployment (#4)
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MiWeiss authored Apr 20, 2022
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6 changes: 3 additions & 3 deletions README.md
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![test](https://github.com/vikpe/python-package-starter/workflows/test/badge.svg?branch=master)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![Docstr-Coverage](https://badgen.net/badge/docstr-coverage/100%25/green)](https://github.com/HunterMcGushion/docstr_coverage)
[![Docstr-Coverage](https://badgen.net/badge/docstr-coverage/100%25/green?cache=30)](https://github.com/HunterMcGushion/docstr_coverage)
[![Python Version](https://img.shields.io/pypi/pyversions/corrupted-text)](https://img.shields.io/pypi/pyversions/corrupted-text)
[![PyPi Deployment](https://badgen.net/pypi/v/corrupted-text)](https://pypi.org/project/corrupted-text/)
[![PyPi Deployment](https://badgen.net/pypi/v/corrupted-text?cache=30)](https://pypi.org/project/corrupted-text/)

A python library to generate out-of-distribution text datasets.
Specifically, the library applies **model-independent**, **commonplace corruptions**
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Usage is very straigthforward.
The following shows an example on how to corrupt the imdb sentiment classification dataset.

You can also run the example in colab: <a class="reference external" href="https://colab.research.google.com/github/testingautomated-usi/corrupted-text/blob/jupyter_example/imdb_example.ipynb"><img alt="Run Example in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
You can also run the example in colab: <a class="reference external" href="https://colab.research.google.com/github/testingautomated-usi/corrupted-text/blob/main/imdb_example.ipynb"><img alt="Run Example in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>


```python
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3 changes: 1 addition & 2 deletions imdb_example.ipynb
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},
"outputs": [],
"source": [
"! pip install polyleven==0.7\n",
"! pip install datasets\n",
"! pip install -i https://test.pypi.org/simple/ corrupted-text==0.1.1"
"! pip install corrupted-text"
]
},
{
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2 changes: 1 addition & 1 deletion setup.py
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return f.read()


MAJOR, MINOR, MICRO = 0, 1, 1
MAJOR, MINOR, MICRO = 0, 2, 0
__VERSION__ = "{}.{}.{}".format(MAJOR, MINOR, MICRO)

setup(
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