vision_transformer VS docarray

And one last thing, should I even be thinking of using CLIP for these tasks when Google has released a better model here: https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md

Google's vision transformers are type hinted.

We’re going to look at a model that Open AI has trained with a broad multilingual dataset: The xlm-roberta-base-ViT-B-32 CLIP model, which uses the ViT-B/32image encoder, and the XLM-RoBERTa multilingual language model. Both of these are pre-trained:

JAX Code: https://github.com/google-research/vision_transformer

For this project I used the pretrained models released by Google in Jax, using this particular PyTorch custom implementation. Those were pretrained on ImageNet21k with 14 M images among 21 K classes. Then yes I finetune on two datasets: one with 15 K images and 170 characters, and one with 3 K characters and almost 500 K images.

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.