Transformers

IGEL (Instruction-based German Language Model) is an LLM designed for German language understanding tasks, including sentiment analysis, language translation, and question answering.

Learn how to leverage Hugging Face Transformers to easily fine-tune your models.

Learn how to fine-tune LiLt (Language independent Layout Transformer) for document-understand/document-parsing using Hugging Face Transformers.

Learn how to deploy T5 11B on a single GPU using Hugging Face Inference Endpoints.

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 LayoutLM for document-understand using Keras & Hugging Face Transformers.

Learn how to deploy LayoutLM for document-understand using Hugging Face Inference Endpoints.

Learn how to fine-tune LayoutLM for document-understand using Hugging Face Transformers. LayoutLM is a document image understanding and information extraction transformers.

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.