Siehe auch Transformer with Spacy

• HuggingFace Official Tutorial
• Hugging-face Credit Managment

The 🤗 Ecosystem

HuggingFace Model Hub

A Tour of 🤗Transformer Applications

HuggingFace Spaces

HuggingFace Tutorials / Courses

Fine-Tuning your custom LLM

Huggingface Agent

🤗Transformers - Bridging the Gap

Transformer Basics

Applying a novel machine learning architecture to a new task can be a complex undertaking, and usually involves the following steps:

• Implement the model architecture in code, typically based on PyTorch or TensorFlow.
• Load the pretrained weights (if available) from a server.
• Preprocess the inputs, pass them through the model, and apply some task-specific postprocessing.
• Implement dataloaders and define loss functions and optimizers to train the model.

This is where a 🤗Transformers comes to the NLP practitioner's rescue! It provides a standardized interface to a wide range of transformer models as well as code and tools to adapt these models to new use cases. The library currently supports three major deep learning frameworks (PyTorch, TensorFlow, and JAX) and allows you to easily switch between them. In addition, it provides task-specific heads so you can easily fine-tune transformers on downstream tasks such as text classification, named entity recognition, and question answering. This reduces the time it takes a practitioner to train and test a handful of models from a week to a single afternoon!