Bert

Learn how to optimize and deploy BERT on AWS Inferentia2

Learn how to fine-tune Hugging Face Transformers using AWS Trainium.

Learn how to quickly set up an AWS Trainium using the Hugging Face Neuron Deep Learning AMI and fine-tune BERT

Learn how to get started with Pytorch 2.0 and Hugging Face Transformers and reduce your training time up to 2x.

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

Learn how to get started with Hugging Face Transformers and TPUs using PyTorch, fine-tune a BERT model for Text Classification using the newest Google Cloud TPUs.

Learn how to deploy a multiple models on to a GPU with Hugging Face multi-model inference endpoints.

Welcome to this tutorial on how to create a custom inference handler for Hugging Face Inference Endpoints.

Learn how to Sentence Transformers model with TensorFlow and Keras for creating document embeddings

Learn how to pre-traing BERT from scratch using Hugging Face Transformers and Habana Gaudi.

Learn how to optimize BERT for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed.

Learn how to optimize Sentence Transformers using Hugging Face Optimum. You will learn how dynamically quantize and optimize a Sentence Transformer for ONNX Runtime.

Learn how to migrate your training jobs to a Habana Gaudi-based DL1 instance on AWS using EC2 Remote Runner.

Learn how to optimize Hugging Face Transformers models for NVIDIA GPUs using Optimum. You will learn how to optimize a DistilBERT for ONNX Runtime

Learn how to learn how to fine-tune XLM-RoBERTa for multi-lingual multi-class text-classification using a Habana Gaudi-based DL1 instance.

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.

Introduction guide about ONNX and Transformers. Learn how to convert transformers like BERT to ONNX and what you can do with it.

Learn how to setup a Deep Learning Environment for Hugging Face Transformers with Habana Gaudi on AWS using the DL1 instance type.

Learn how to do post-training static quantization on Hugging Face Transformers model with `optimum` to achieve up to 3x latency improvements.

Learn how to do advanced PII detection and anonymization with Hugging Face Transformers and Amazon SageMaker.

Learn about the different existing Amazon SageMaker Inference options and and how to use them.

Learn how to deploy a Transformer model like BERT to Amazon SageMaker Serverless using the Python SageMaker SDK.

Learn how to accelerate Sentence Transformers inference inference using Hugging Face Transformers and AWS Inferentia.

Learn how to leverage AWS Spot Instances when training Hugging Face Transformers with Amazon SageMaker to save up to 90% training cost.

Learn how to accelerate BERT and Transformers inference using Hugging Face Transformers and AWS Inferentia.

Learn how to use a custom Inference script for creating document embeddings with Hugging Face's Transformers, Amazon SageMaker, and Sentence Transformers.

Learn how to apply autoscaling to Hugging Face Transformers and Amazon SageMaker using Terraform.

Learn how to deploy multiple Hugging Face Transformers for inference with Amazon SageMaker and Multi-Container Endpoints.

Learn how to deploy an Asynchronous Inference model with Hugging Face Transformers and Amazon SageMaker, with autoscaling to zero.

Learn how to deploy BERT/DistilBERT with Hugging Face Transformers using Amazon SageMaker and Terraform module.

Learn how to run apply task-specific knowledge distillation for BERT and text-classification using Hugging Face Transformers & Amazon SageMaker including Hyperparameter search.

Learn how to run large-scale distributed training using multilingual BERT on over 1 million data points with Hugging Face Transformers & Amazon SageMaker

Learn how to fine-tune a non-English BERT using Hugging Face Transformers and Keras/TF, Transformers, datasets.

Learn how to deploy Hugging Face Transformers serverless using the new Amazon SageMaker Serverless Inference.

Learn how to Compile and fine-tune a Multi-Class Classification Transformers with `Trainer` and `emotion` dataset using Amazon SageMaker Training Compiler.

Learn how to automatically save your model weights, logs, and artifacts to the Hugging Face Hub using Amazon SageMaker and how to deploy the model afterwards for inference.

Learn how to build an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker.

Learn how to add auto-scaling to your Hugging Face Transformers SageMaker Endpoints.

Deploy Hugging Face Transformers to Amazon SageMaker and create an API for the Endpoint using AWS Lambda, API Gateway and AWS CDK.

Learn how to build a Multilingual Serverless BERT Question Answering API with a model size of more than 2GB and then testing it in German and France.

Learn how to use the newest cutting edge computing power of AWS with the benefits of serverless architectures to leverage Google's "State-of-the-Art" NLP Model.

Build a serverless Question-Answering API using the Serverless Framework, AWS Lambda, AWS EFS, efsync, Terraform, the transformers Library from HuggingFace, and a `mobileBert` model from Google fine-tuned on SQuADv2.

Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework and AWS Lambda.

Build a non-English (German) BERT multi-class text classification model with HuggingFace and Simple Transformers.

Using the K-Fold Cross-Validation to improve your Transformers model validation by the example of BERT Text-Classification