Hashtagify transformation #246
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ashish3586
reviewed
Sep 8, 2021
| tasks = [ | ||
| TaskType.TEXT_CLASSIFICATION, | ||
| TaskType.TEXT_TO_TEXT_GENERATION, | ||
| TaskType.TEXT_TAGGING, |
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TEXT_TAGGING will not be applicable here due to the difference in the number of tokens between the input sentence and the transformed sentence.(New Delhi --> #NewDelhi)
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I agree. It can be removed.
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Should I remove it and commit again?
…r/NL-Augmenter into hashtagify_transformation
| nltkdl("maxent_ne_chunker") | ||
| nltkdl("punkt") | ||
| nltkdl("averaged_perceptron_tagger") | ||
| nltkdl("stopwords") |
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You might want to move this to the constructor.
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| def __init__(self, seed=666, max_outputs=1): | ||
| super().__init__(seed, max_outputs=max_outputs) | ||
| self.nlp = spacy.load("en_core_web_sm") |
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You can use spacy like this
| #NewDelhi is among the many famous places in India. | ||
| ``` | ||
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I would suggest to add the robustness evaluation section too.
ashish3586
reviewed
Sep 29, 2021
| return perturbed_texts | ||
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| class HashtagifyTransformation(SentenceOperation): |
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Please add docstring for the class HashtagifyTransformation.
mille-s
approved these changes
Sep 30, 2021
kaustubhdhole
approved these changes
Oct 1, 2021
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This transformation adapts an input sentence by identifying named entities and other common words and turning them into hashtags, as often used in social media.