The first step in natural language processing is creating word-numbers, represented as points in space. If this confuses you, you're not alone. Keep reading.| The Content Technologist
In this post, we show you how to run Habana’s DeepSpeed enabled BERT1.5B model from our Model-References repository.| Habana Developers
Learn how to quickly set up an AWS Trainium using the Hugging Face Neuron Deep Learning AMI and fine-tune BERT| www.philschmid.de
Learn how to setup a Deep Learning Environment for Hugging Face Transformers with Habana Gaudi on AWS using the DL1 instance type.| www.philschmid.de
Learn how to get started with Pytorch 2.0 and Hugging Face Transformers and reduce your training time up to 2x.| www.philschmid.de
Welcome to this tutorial on how to create a custom inference handler for Hugging Face Inference Endpoints.| www.philschmid.de
Learn how to Sentence Transformers model with TensorFlow and Keras for creating document embeddings| www.philschmid.de
Learn how to optimize Hugging Face Transformers models for NVIDIA GPUs using Optimum. You will learn how to optimize a DistilBERT for ONNX Runtime| www.philschmid.de
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.| www.philschmid.de
Introduction guide about ONNX and Transformers. Learn how to convert transformers like BERT to ONNX and what you can do with it.| www.philschmid.de