Tabular data is everywhere, from spreadsheets to tables embedded in text documents. In this Hugging Face Space, you can use the Google TAPAS and Microsoft TAPEX models to ask questions in natural language on CSV data. Give it a try and let me know what you think :)
The Hugging Face hub hosts 1,700+ image classification models. I’ve picked the top ones from Google, Meta, Microsoft, NVIDIA and Humphrey Shi. All of them have been fine-funed on the ImageNet-1k dataset, which makes it easy to compare their performance on your images. Enjoy!
Space: https://huggingface.co/spaces/juliensimon/battle_of_image_classifiers
Multi-modal transformers are rising fast. A great example is the Audio Spectrogram Transformer, an audio classification model that was just added to the Hugging Face Transformers library. This model first creates a spectrogram image of an audio clip and then classifies the image with a Vision Transformer model. Amazing results!
✅ Spaces demo: https://huggingface.co/spaces/juliensimon/keyword-spotting
✅ Model: https://huggingface.co/MIT/ast-finetuned-speech-commands-v2
✅ Paper: https://arxiv.org/abs/2104.01778
What if you could ask questions on HTML documents, without having to convert them to plain text first? Well, that’s exactly the purpose of the Microsoft MarkupLM: just grab a page and ask a question.
I’ve built a Hugging Face Space to let you experiment with any live URL. I also implemented multithreading to speed things up on CPU. Give it a go and let me know what you think :)
✅ Space: https://huggingface.co/spaces/juliensimon/webpage_questions
✅ Model: https://huggingface.co/microsoft/markuplm-base-finetuned-websrc
✅ Paper: https://arxiv.org/abs/2110.08518
In this video, you will learn how to accelerate a PyTorch training job with a cluster of Intel Sapphire Rapids servers running on AWS. We will use the Intel oneAPI Collective Communications Library (CCL) to distribute the job, and the Intel Extension for PyTorch (IPEX) library to automatically put the new CPU instructions to work. As both libraries are already integrated with the Hugging Face transformers library, we will be able to run our sample scripts out of the box without changing a line of code.
In this video, I show you how to use Amazon SageMaker to train a Transformer model with AWS Trainium and compile it for AWS Inferentia.
Starting from a BERT model and the Yelp review datatset, I first train a multi-class classification model on an ml.trn1.2xlarge instance. I also show you…
In this video, I show you how to accelerate Transformer inference with Optimum, an open-source library by Hugging Face, and Better Transformer, a PyTorch extension available since PyTorch 1.12.
Using an AWS instance equipped with an NVIDIA V100 GPU, I start from a couple of models that I previously fine-tuned: a DistilBERT model for text classification and a Vision Transformer model for image classification. I first benchmark the original models, then I use Optimum and Better Transformer to optimize them with a single line of code, and I benchmark them again. This simple process delivers a 20–30% percent speedup with no accuracy drop!
In this video, I show you how to accelerate Transformer inference with Inferentia, a custom chip designed by AWS.
Starting from a Hugging Face BERT model that I fine-tuned on AWS Trainium (https://youtu.be/HweP7OYNiIA), I compile it with the Neuron SDK for Inferentia. Then, using an inf1.6xlarge instance (4 Inferentia chips, 16 Neuron Cores), I show you how to use pipeline mode to predict at scale, reaching over 4,000 predictions per second at 3-millisecond latency 🤘
