OCTIS
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| OCTIS | TopMost | |
|---|---|---|
| 7 | 1 | |
| 685 | 140 | |
| 1.0% | - | |
| 6.0 | 7.6 | |
| 4 months ago | about 1 month ago | |
| Python | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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OCTIS
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Interpretation of topic modeling results between LDA and BERTopic
OCTIS
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(NLP) Best practices for topic modeling and generating interesting topics?
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
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I am working on a topic modelling paper and I need your help
I recently released a topic modeling library that also includes different evaluation measures. If you are interested, I leave here the link: https://github.com/mind-Lab/octis
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Latest trends in topic modelling?
Silvia Terragni (a coauthor on the above) also brought a topic modelling library OCTIS which was exhibited as a demo paper and aims to be the huggingface transformers of topic modelling - it includes wrappers around the above model as well as and LDA and some baselines as well as some tools and frameworks for comparing them.
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OCTIS a python framework to compare and optimize Topic Models
Link to the code Paper
- OCTIS, our new python framework to optimize and compare topic models has been accepted at EACL2021!
- [p] OCTIS: Optimizing and Comparing Topic models Is Simple. Our new python framework to compare and optimize topic models using Bayesian Optimization
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What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
converse - Conversational text Analysis using various NLP techniques
auto-sklearn - Automated Machine Learning with scikit-learn
image-similarity-measures - :chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
medspacy - Library for clinical NLP with spaCy.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
rakun2 - RaKUn 2.0 - A fast keyword detection algorithm
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.