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        "author": "Dominika Basaj, Barbara Rychalska, Alicja Gosiewska, Adam S\u0142ucki",
        "author_email": "adam.slucki@gmail.com",
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            "License :: OSI Approved :: BSD License",
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        "description": "[![Documentation Status](https://readthedocs.org/projects/nlp-demo/badge/?version=latest)](https://nlp-demo.readthedocs.io/en/latest/?badge=latest)\n[![Downloads](https://pepy.tech/badge/wild-nlp)](https://pepy.tech/project/wild-nlp)\n[![PyPI version](https://badge.fury.io/py/wild-nlp.svg)](https://badge.fury.io/py/wild-nlp)\n\n![alt wildnlp-logo](logo.png)  \n\nCorrupt an input text to test NLP models' robustness.  \nFor details refer to https://nlp-demo.readthedocs.io\n\n## Installation\n`pip install wild-nlp`\n\n## Supported aspects\nAll together we defined and implemented 11 aspects of text corruption.\n\n1. **Articles**\n\n   Randomly removes or swaps articles into wrong ones.\n\n2. **Digits2Words**\n\n   Converts numbers into words. Handles floating numbers as well.\n\n3. **Misspellings**\n\n   Misspells words appearing in the Wikipedia list of:  \n    * **commonly misspelled English words**  \n    * **homophones**\n\n4. **Punctuation**\n\n   Randomly adds or removes specified punctuation marks.\n\n5. **QWERTY**\n\n   Simulates errors made while writing on a QWERTY-type keyboard.\n\n6. **RemoveChar**\n\n   Randomly removes:  \n   * **characters** from words or  \n   * **white spaces** from sentences\n\n7. **SentimentMasking**\n\n   Replaces random, single character with for example an asterisk in:  \n   * **negative** or  \n   * **positive** words from Opinion Lexicon:    \n   http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html\n\n8. **Swap**\n\n   Randomly swaps two characters within a word, excluding punctuations.\n\n9. **Change char**\n\n   Randomly change characters according to chosen dictionary, default is 'ocr' to simulate simple OCR errors.\n\n10. **White spaces**\n\n   Randomly add or remove white spaces (listed as a parameter).\n\n11. **Sub string**\n\n   Randomly add a substring to simulate more comples signs.\n\n\n```diff\n- All aspects can be chained together with the wildnlp.aspects.utils.compose function.\n```\n\n## Supported datasets\nAspects can be applied to any text. Below is the list of datasets for which we already implemented processing pipelines. \n\n1. **CoNLL**\n\n   The CoNLL-2003 shared task data for language-independent named entity recognition.\n\n2. **IMDB**\n\n   The IMDB dataset containing movie reviews for a sentiment analysis. The dataset consists of 50 000 reviews of two classes, negative and positive.\n\n3. **SNLI**\n\n   The SNLI dataset supporting the task of natural language inference.\n\n4. **SQuAD**\n\n   The SQuAD dataset for the Machine Comprehension problem.\n\n## Usage\n```python\nfrom wildnlp.aspects.dummy import Reverser, PigLatin\nfrom wildnlp.aspects.utils import compose\nfrom wildnlp.datasets import SampleDataset\n\n# Create a dataset object and load the dataset\ndataset = SampleDataset()\ndataset.load()\n\n# Crate a composed corruptor function.\n# Functions will be applied in the same order they appear.\ncomposed = compose(Reverser(), PigLatin())\n\n# Apply the function to the dataset\nmodified = dataset.apply(composed)\n```\n\n\n",
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