jouresearch-nlp
A python package for generating topics, named entities and a wordcloud visualization. It leverages the SpaCy framework and sentence transformers. (by joureka-ai)
OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track) (by MIND-Lab)
| jouresearch-nlp | OCTIS | |
|---|---|---|
| 1 | 7 | |
| 3 | 685 | |
| - | 1.0% | |
| 10.0 | 6.0 | |
| almost 2 years ago | 4 months ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
jouresearch-nlp
Posts with mentions or reviews of jouresearch-nlp.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-05.
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Looking for career advice as beginner Python developer with NLP and Backend experience
As a Freelancer - Developed a package for providing meaningful insights on text for journalists (Open Source version of it: https://github.com/joureka-ai/jouresearch-nlp)
OCTIS
Posts with mentions or reviews of OCTIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-31.
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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
What are some alternatives?
When comparing jouresearch-nlp and OCTIS you can also consider the following projects:
IDEA - Text Data Visualizer with Django
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
joureka-app - joureka - Mit mehr Muße vom Interview zum Artikel!
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.
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.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
TopMost - A Topic Modeling System Toolkit