aryashah2k/NLP-Data-Augmentation
Implementing 5 Different Approaches To Augmenting Data For Natural Language Processing Tasks.
This helps machine learning engineers and data scientists expand their text datasets when they don't have enough data to train a robust natural language processing model. You input your existing text data, and it outputs new, varied text examples that are still relevant to your original dataset, making your models more accurate and reliable. This is for anyone building or improving NLP models.
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Use this if you have a limited amount of text data and need to generate more diverse training examples to improve the performance of your NLP models.
Not ideal if you already have a very large, diverse text dataset, or if your primary goal is to label data rather than augment it.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 25, 2022
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