dopamine
nlpaug
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dopamine | nlpaug | |
---|---|---|
3 | 10 | |
10,342 | 4,252 | |
0.4% | - | |
3.2 | 0.0 | |
4 months ago | about 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dopamine
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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RL review
You can also reference the source code for some of the popular implementations from open source RL libraries like stablebaselines3, RLlib, CleanRL, or Dopamine. These can help you if youβre trying to compare your implementation to a βstandardβ.
- Rainbow Library
nlpaug
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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.
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[R] Call for Participation to NL-Augmenter π¦ β π
Are there any shortfalls in nlpaug which justified another project?
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A Visual Survey of Data Augmentation in NLP
Libraries like nlpaug and textattack provide simple and consistent API to apply the above NLP data augmentation methods in Python. They are framework agnostic and can be easily integrated into your pipeline.
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.
What are some alternatives?
spaCy - π« Industrial-strength Natural Language Processing (NLP) in Python
NL-Augmenter - NL-Augmenter π¦ β π A Collaborative Repository of Natural Language Transformations
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
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
imodels - Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
advertorch - A Toolbox for Adversarial Robustness Research
airline-sentiment-streaming - Streaming with Airline Sentiment. Utilizing Cloudera Machine Learning, Apache NiFi, Apache Hue, Apache Impala, Apache Kudu
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
contractions - Fixes contractions such as `you're` to `you are`
diffusion_models - Minimal standalone example of diffusion model
uda - Unsupervised Data Augmentation (UDA)