Uncovering Hidden Gems: The 5 Most Underrated Tools on Hugging Face That Boost Your NLP Performance

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Use-Case

An automatically updated, visually enabled, semantic search for a dynamic data source, for free

• E-commerce platforms that are looking to handle their many products based on descriptions or reported issues
• Law firms and compliance departments who need to comb through legal documents or regulations
• Researchers who have to keep up with new advances and find relevant papers or articles for their needs

ZerosGPU

One of the challenges with modern models is they typically require GPUs or other heavy hardware to run. These can be bulky with year long commitments and very expensive. Spaces makes it easy to use the hardware you desire at a low cost, but it’s not automatically spun up and down (though you could programmatically do it!) ZeroGPU is a new kind of hardware for Spaces. There is a quota for free users and a bigger one for PRO users.

ZerosGPU: Behind the Scenes

Figure 2: ZeroGPU behind the scenes

This is achieved by making Spaces efficiently hold and release GPUs as needed (as opposed to a classical GPU Space with a GPU attached at all times). ZeroGPU uses Nvidia A100 GPUs under the hood (40GB of vRAM are available for each workload).

Application

I used ZeroGPU to host the amazing nomic embedding model in my Embedding Model Space. It’s super convenient because I don’t really need a dedicated GPU as I only need to do inference occasionally and incrementally.

It’s extremely simple to use. The only change is that you need to have a function with all your GPU code inside, and decorate that with @spaces.GPU.

Multi-process Docker

Using Spaces makes it easy to use the hardware you desire at a low cost, but it’s not automatically spun up and down (though you could programmatically do it!).

Example Code

import spaces

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True, device='cuda')

@spaces.GPU
def embed(document: str):
    return model.encode(document)

Gradio API

I used Gradio to create a visual interface for my Embedding Model Space. It’s super convenient because I can now easily explore the documents I uploaded.

Nomic Atlas

Using Nomic Atlas allows you to easily search and visualize your embedded documents. You can search by title, author, and date, as well as visualize the documents in different ways.

Frequently Asked Questions

Question 1: What is ZeroGPU?

ZeroGPU is a new kind of hardware for Spaces. There is a quota for free users and a bigger one for PRO users.

Question 2: How do I use ZeroGPU?

You simply need to decorate your function with @spaces.GPU. This will allow you to easily use ZeroGPU for your project.

Question 3: What is Gradio API?

Gradio API allows you to easily create a visual interface for your spaces. This is especially useful for exploring and analyzing your embedded documents.

Question 4: How do I create an Nomic Atlas?

To create an Nomic Atlas, simply use the nomic atlas.py script. This will allow you to create an atlas with the desired settings and dimensions.

Question 5: Can I customize my Nomic Atlas?

Yes, you can customize your Nomic Atlas by using various settings and options. This includes being able to customize the visualization, search function, and more.

Conclusion

By understanding the lesser-known tools and features of the Hugging Face Hub, you can build more efficient, powerful, and innovative applications. Whether you are working on a project or just curious about what the Hub has to offer, these tools can help you get started. Happy building!