nlpaug
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| nlpaug | advertorch | |
|---|---|---|
| 10 | 1 | |
| 4,252 | 1,271 | |
| - | 1.2% | |
| 0.0 | 0.0 | |
| about 1 year ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU Lesser General Public License v3.0 only |
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nlpaug
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Use WordNet to collect homonyms
You'd want to use an NLP method for this as in order to determine optimal homonyms there would have to be some method of deriving context from the words ahead of and behind the substitution. Take a look at nlpaug.
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Contextual Similarity between a list of n-grams and a website
3) Use deep contextual models with wordpiecing/BPE tokenizers- like all the models: BERT, RoBERTA, etc. On the simpler side, could also swap words with synonyms, which is easy to do with this library: https://github.com/makcedward/nlpaug. Instead of a single n-gram per topic, it might be nice to have a bundle of related words- you could play around with wordnet and see if that's helpful- also easy to do w/ nlpaug.
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Word embeddings / language models for synonym generation?
In practice, even swapping words with dictionary synonyms is a problem because context isn't considered. Lexically sensitive contextual augmentation has become more popular in the last year or two - basically you mask a token using a large language model and then use the model to predict it so it has the full context. It's imperfect, but it's surprisingly useful when you want to upsample data. Nlpaug has an easy-to-use implementation https://github.com/makcedward/nlpaug
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Text Data Augmentation using GPT-2 Language Model
A cool library I recently came across for text augmentation is nlpaug, it does a different thing to your approach, but I think both are useful :)
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[D] Data Augmentation in NLP
This is a nice starting point: https://github.com/makcedward/nlpaug
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NLPAug: what proportion of augmented sentences do you usually add to the dataset?
Since the dataset is relatively tiny, we are working on augmenting it with NLPAug. We use 2 strategies. Synonymisation and back translation.
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Show HN: 40k Book Recommendations on HN Extracted Using Deep Learning
Thank you!
The medium post is amazingly written! I basically did the same thing - and you beat me with the data augmentation piece. I tried using nlpaug [0] but it didn't improve the model performance. I'll definitely try swapping book titles around.
[0] https://github.com/makcedward/nlpaug
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[R] Call for Participation to NL-Augmenter 🦎 → 🐍
Are there any shortfalls in nlpaug which justified another project?
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A Visual Survey of Data Augmentation in NLP
Spelling error injection In this method, we add spelling errors to some random word in the sentence. These spelling errors can be added programmatically or using a mapping of common spelling errors such as this list for English.
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What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
NL-Augmenter - NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
Tic-Tac-Toe-Gym - This is the Tic-Tac-Toe game made with Python using the PyGame library and the Gym library to implement the AI with Reinforcement Learning
mlattacks - Machine Learning Attack Series
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
AugMax - [NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
timm-vis - Visualizer for PyTorch image models
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
diffusion_models - Minimal standalone example of diffusion model