chai_py
gensim
| chai_py | gensim | |
|---|---|---|
| 3 | 18 | |
| 60 | 15,256 | |
| - | 0.9% | |
| 0.0 | 7.5 | |
| about 1 year ago | 10 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU Lesser General Public License v3.0 only |
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chai_py
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WHAAATTT! Since when the bots can do this? How to use it?
The package is here: https://github.com/chai-research/chai_py Quoted directly from the documentation: "The bot response accepts markdown and so you can include an image like "
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Developer Docs
Worth noting the code is not open source. As you can see here, the inside of the functions are empty : https://github.com/chai-nexus/chai_py/blob/main/chai_py/chai_bot.py But it may be possible to get its real content with the inspect module
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Is there an updated Python API in the works by chance?
The latest release is 0.4.0 from about a year ago. This looks abandoned ( https://github.com/chai-nexus/chai_py and https://pypi.org/project/chaipy/ ) and doens't work for even simply operations like retrieving the bot list. (yes with authentication of course; I have 1 bot deployed and that call bombs with a 500 status claiming "Payload is too large")
gensim
- Aggregating news from different sources
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Understanding How Dynamic node2vec Works on Streaming Data
This is our optimization problem. Now, we hope that you have an idea of what our goal is. Luckily for us, this is already implemented in a Python module called gensim. Yes, these guys are brilliant in natural language processing and we will make use of it. 🤝
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Topic modeling --- allow multiple topics per statement
Try LDA as implemented in gemsin https://github.com/RaRe-Technologies/gensim
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Is it home bias or is data wrangling for machine learning in python much less intuitive and much more burdensome than in R?
Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling.
- sentence transformer vector dimensionality reduction to 1
- Where to start for recommendation systems
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GET STARTED WITH TOPIC MODELLING USING GENSIM IN NLP
Here we have to install the gensim library in a jupyter notebook to be able to use it in our project, consider the code below;
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Show HN: I built a site that summarizes articles and PDFs using NLP
Nice work! I wonder if you're going the same challenges that gensim had for being generic in summarization.
For context:
> Despite its general-sounding name, the module will not satisfy the majority of use cases in production and is likely to waste people's time.
https://github.com/RaRe-Technologies/gensim/wiki/Migrating-f...
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[Research] Text summarization using Python, that can run on Android devices?
TextRank will work without any problems. https://radimrehurek.com/gensim/
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Topic modelling with Gensim and SpaCy on startup news
For the topic modelling itself, I am going to use Gensim library by Radim Rehurek, which is very developer friendly and easy to use.
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
MLflow - Open source platform for the machine learning lifecycle
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Keras - Deep Learning for humans
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)