PyABSA
100DaysofMLCode
PyABSA | 100DaysofMLCode | |
---|---|---|
2 | 1 | |
874 | 303 | |
- | - | |
5.1 | 0.0 | |
1 day ago | 9 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | 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.
PyABSA
-
Extracting targeted sentiment from from product reviews
Check out: https://github.com/yangheng95/PyABSA
-
[D] How to best extract product benefits/problems from customer reviews using NLP?
https://github.com/yangheng95/PyABSA - for extraction of aspects + corresponding sentiments
100DaysofMLCode
-
#100DaysofMLCode Challenge
NishkarshRaj / 100DaysofMLCode
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
100-Days-Of-ML-Code - 100 Days of ML Coding
MAPIE - A scikit-learn-compatible module for estimating prediction intervals.
hdbscan - A high performance implementation of HDBSCAN clustering.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
rmi - A learned index structure
SelSum - Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.
rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
vqgan-clip-generator - Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.
glasgow-litter - A project that explores the relationship between deprivation and litter in Glasgow City. 🚯
notebooks - Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.