nlpaug
azureml-examples
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| nlpaug | azureml-examples | |
|---|---|---|
| 10 | 4 | |
| 4,252 | 1,554 | |
| - | 5.0% | |
| 0.0 | 9.6 | |
| about 1 year ago | 3 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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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.
azureml-examples
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How to deploy ML models on Azure Kubernetes Service (AKS)
If you need a reference on how these files should look, you can get a dummy model, env and scoring script here. Optionally, you can also check out my GitHub for the code used to deploy via the Python SDK v2.
- Best way to run my Python project on Azure
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How to get around "package ‘AzureML’ is not available (for R version 4.0.2) "
It seems like there's a new paradigm for R on Azure that uses Docker and job.yml to tell R how to execute your "vanilla" R code without the need for a package. There are examples here: https://github.com/Azure/azureml-examples/tree/0849cbe797d1d524df9fe9d43ac8b36e75ea34ab/cli/jobs/train/r
- Machine learning in Azure
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
pycaret - An open-source, low-code machine learning library in Python
NL-Augmenter - NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations
MachineLearningNotebooks - Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
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
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
advertorch - A Toolbox for Adversarial Robustness Research
Understanding_the_EM_Algorithm - Codes for my blog post "Understanding the EM Algorithm" https://mistylight.github.io/posts/20115/
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
pedestrian-intent-prediction - Repository for master thesis at the Chalmers University of Technology