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Preprocess
[[open-in-colab]]
Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare... |
Run training on Amazon SageMaker
The documentation has been moved to hf.co/docs/sagemaker. This page will be removed in transformers 5.0.
Table of Content
Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK
Deploy Hugging Face models to Amazon SageMaker with the SageMaker Python SDK
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How to convert a 🤗 Transformers model to TensorFlow?
Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when
designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that
adding TensorFlow compatibili... |
Share a model
The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We... |
GPU inference
GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed ... |
Efficient Training on Multiple CPUs
When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling
distributed CPU training efficiently on bare metal and Kubernetes.
Intel® oneCCL Bindings for PyTorch
Intel® oneCCL (collective communications library) is a library ... |
Custom Tools and Prompts
If you are not aware of what tools and agents are in the context of transformers, we recommend you read the
Transformers Agents page first.
Transformers Agents is an experimental API that is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying mode... |
Padding and truncation
Batched inputs are often different lengths, so they can't be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special padding token to ensure shorter sequences will ... |
Distributed training with 🤗 Accelerate
As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on ... |
Community
This page regroups resources around 🤗 Transformers developed by the community.
Community resources:
| Resource | Description | Author |
|:----------|:-------------|------:|
| Hugging Face Transformers Glossary Flashcards | A set of flashcards based on the Transformers Docs Glossary t... |
Troubleshoot
Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try:
Asking for help o... |
Export to ONNX
Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into
a serialized format that can be loaded and executed on specialized runtimes and hardware.
🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or Te... |
Fine-tune a pretrained model
[[open-in-colab]]
There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models for a... |
How 🤗 Transformers solve tasks
In What 🤗 Transformers can do, you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the hood. There are many ... |
Benchmarks
Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed
and memory complexity of Transformer models.
[[open-in-colab]]
Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmark... |
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