imodels VS docarray

- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications

Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.

Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF

Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading

Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!

I’ve heard of DocArray, a library for multimodal data in transit and Pytorch Lightning which is also a tool for multimodal data. These two sound like a promising solution, but I’m not sure how to use it with databases or cloud storage. Do I need to install any additional packages or dependencies?

I'm considering using these tools for a recommender system for analyzing text data like user reviews: DocArray and the EZ-MMLA Toolkit. Can anyone share their experience with the DocArray and EZ-MMLA Toolkit? I would love to hear about others' experiences before making a final decision.

I have been thinking about trying out DocArray and the EZ-MMLA Toolkit .. Has anyone had experience with these two projects?? Let me know what you think!

For some alternatives, I know there’s DocArray where you can handle text, image and audio data. is basically a toolbox for multimodal data and then there should be Haystack which is also let you build search systems and also has to do something with Transformers and LLMs.

DocArray to manipulate data and interact with the storage backend using document store.

It's already been two weeks since the last alpha release of DocArray v2. And since then a lot has happened — we've merged features we're really proud of, and we've cried tears of joy and misery trying to coerce Python into doing what we want. If you want to learn about interesting Python edge cases or follow the advancement of DocArray v2 development then you’ve come to the right place in this blog post!

The German Fashion12k dataset is available for free use by the Jina AI community. After logging into Jina AI Cloud, you can download it directly in DocArray format:

Jina AI’s DocArray library

You could try spaCy. This is the brains of the operation - an open-source NLP library for advanced NLP in Python. Another is DocArray - It's built on top of NumPy and Dask, and good for preprocessing, modeling, and analysis of text data.