Learn how to use SetFit to create a text-classification model with only a `8` labeled samples per class, or `32` samples in total. You will also learn how to improve your model by using hyperparamter tuning.
Learn how to fine-tune Donut-base for document-understand/document-parsing using Hugging Face Transformers. Donut is a new document-understanding model achieving state-of-art performance and can be used for commercial applications.
Learn how to optimize Hugging Face Transformers models using Optimum. The session will show you how to dynamically quantize and optimize a DistilBERT model using Hugging Face Optimum and ONNX Runtime. Hugging Face Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardware.
In October and November, we held a workshop series on “Enterprise-Scale NLP with Hugging Face & Amazon SageMaker”. This workshop series consisted out of 3 parts and covers: Getting Started, Going Production & MLOps.
🌸 BigScience released their first modeling paper introducing T0 which outperforms GPT-3 on many zero-shot tasks while being 16x smaller! Deploy BigScience the 3 Billion version (T0_3B) to Amazon SageMaker with a few lines of code to run a scalable production workload!
The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo.