docarray VS dataqa

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.

Hey, I am the creator and (only contributor today) of open-source https://github.com/dataqa/dataqa, a Python library to explore and annotate documents. It uses weak supervision, is based on spacy, and has a lot of opportunities to add more deep learning and ML functionality. I can guide you through it :-). This would be a great opportunity to be first and lead contributor of an open-source library (outside the creator).

I recently added a new feature to DataQA (https://github.com/dataqa/dataqa) to be able to extract entities from Wikipedia. All you need to do is upload a file with Wikipedia urls:

The open-source project is here: https://github.com/dataqa/dataqa. I have just released a feature which I have been working on for a while to solve a problem which I've seen a lot in industry: how to map entities found in text to large knowledge base ontologies.

The tool I developed and used for this problem: https://github.com/dataqa/dataqa

I have seen first hand at small and large companies how problems have been tackled with ML without trying a simple rule or heuristic first. And then, further down the line, the system has been compared to a few business rules put together, to find that the difference in performance did not explain the deployment of an ML system in the first place.

It's true that if your rules grow in complexity, this might make it harder to maintain, but the good thing about rules is that they tend to be fully explainable, and they can be encoded by domain experts. So the maintenance of such a system does not need to be done exclusively by an ML engineer anymore.

Here is where I insert my plug: I have developed a tool to create rules to solve NLP problems: https://github.com/dataqa/dataqa

After working in ML for more than a decade, I became frustrated over time with the lack of tools to create baselines using simple rules and heuristics. It is well known that most business problems out there can achieve decent baselines using only heuristics. This is why I have developed DataQA (https://github.com/dataqa/dataqa), which uses NLP rules to do common NLP annotation tasks, such as multiclass classification or named entity recognition.