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Text aspects for nlp models

Project description

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Corrupt an input text to test NLP models' robustness.
For details refer to https://nlp-demo.readthedocs.io

Installation

pip install wild-nlp

Supported aspects

All together we defined and implemented 11 aspects of text corruption.

  1. Articles

    Randomly removes or swaps articles into wrong ones.

  2. Digits2Words

    Converts numbers into words. Handles floating numbers as well.

  3. Misspellings

    Misspells words appearing in the Wikipedia list of:

    • commonly misspelled English words
    • homophones
  4. Punctuation

    Randomly adds or removes specified punctuation marks.

  5. QWERTY

    Simulates errors made while writing on a QWERTY-type keyboard.

  6. RemoveChar

    Randomly removes:

    • characters from words or
    • white spaces from sentences
  7. SentimentMasking

    Replaces random, single character with for example an asterisk in:

  8. Swap

    Randomly swaps two characters within a word, excluding punctuations.

  9. Change char

    Randomly change characters according to chosen dictionary, default is 'ocr' to simulate simple OCR errors.

  10. White spaces

Randomly add or remove white spaces (listed as a parameter).

  1. Sub string

Randomly add a substring to simulate more comples signs.

- All aspects can be chained together with the wildnlp.aspects.utils.compose function.

Supported datasets

Aspects can be applied to any text. Below is the list of datasets for which we already implemented processing pipelines.

  1. CoNLL

    The CoNLL-2003 shared task data for language-independent named entity recognition.

  2. IMDB

    The IMDB dataset containing movie reviews for a sentiment analysis. The dataset consists of 50 000 reviews of two classes, negative and positive.

  3. SNLI

    The SNLI dataset supporting the task of natural language inference.

  4. SQuAD

    The SQuAD dataset for the Machine Comprehension problem.

Usage

from wildnlp.aspects.dummy import Reverser, PigLatin
from wildnlp.aspects.utils import compose
from wildnlp.datasets import SampleDataset

# Create a dataset object and load the dataset
dataset = SampleDataset()
dataset.load()

# Crate a composed corruptor function.
# Functions will be applied in the same order they appear.
composed = compose(Reverser(), PigLatin())

# Apply the function to the dataset
modified = dataset.apply(composed)

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