🤗 Transformers Notebooks

You can find here a list of the official notebooks provided by Hugging Face.

Also, we would like to list here interesting content created by the community. If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a Pull Request so it can be included under the Community notebooks.

Hugging Face's notebooks 🤗

Documentation notebooks

You can open any page of the documentation as a notebook in colab (there is a button directly on said pages) but they are also listed here if you need to:

Notebook	Description
Quicktour of the library	A presentation of the various APIs in Transformers
Summary of the tasks	How to run the models of the Transformers library task by task
Preprocessing data	How to use a tokenizer to preprocess your data
Fine-tuning a pretrained model	How to use the Trainer to fine-tune a pretrained model
Summary of the tokenizers	The differences between the tokenizers algorithm
Multilingual models	How to use the multilingual models of the library

PyTorch Examples

Notebook	Description
Train your tokenizer	How to train and use your very own tokenizer
Train your language model	How to easily start using transformers
How to fine-tune a model on text classification	Show how to preprocess the data and fine-tune a pretrained model on any GLUE task.
How to fine-tune a model on language modeling	Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task.
How to fine-tune a model on token classification	Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS).
How to fine-tune a model on question answering	Show how to preprocess the data and fine-tune a pretrained model on SQUAD.
How to fine-tune a model on multiple choice	Show how to preprocess the data and fine-tune a pretrained model on SWAG.
How to fine-tune a model on translation	Show how to preprocess the data and fine-tune a pretrained model on WMT.
How to fine-tune a model on summarization	Show how to preprocess the data and fine-tune a pretrained model on XSUM.
How to fine-tune a speech recognition model in English	Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT
How to fine-tune a speech recognition model in any language	Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice
How to fine-tune a model on audio classification	Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting
How to train a language model from scratch	Highlight all the steps to effectively train Transformer model on custom data
How to generate text	How to use different decoding methods for language generation with transformers
How to generate text (with constraints)	How to guide language generation with user-provided constraints
How to export model to ONNX	Highlight how to export and run inference workloads through ONNX
How to use Benchmarks	How to benchmark models with transformers
Reformer	How Reformer pushes the limits of language modeling
How to fine-tune a model on image classification (Torchvision)	Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification
How to fine-tune a model on image classification (Albumentations)	Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification
How to perform zero-shot object detection with OWL-ViT	Show how to perform zero-shot object detection on images with text queries
How to fine-tune a pre-trained protein model	See how to tokenize proteins and fine-tune a large pre-trained protein “language” model
How to generate protein folds	See how to go from protein sequence to a full protein model and PDB file

TensorFlow Examples

Notebook	Description
Train your tokenizer	How to train and use your very own tokenizer
Train your language model	How to easily start using transformers
How to fine-tune a model on text classification	Show how to preprocess the data and fine-tune a pretrained model on any GLUE task.
How to fine-tune a model on language modeling	Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task.
How to fine-tune a model on token classification	Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS).
How to fine-tune a model on question answering	Show how to preprocess the data and fine-tune a pretrained model on SQUAD.
How to fine-tune a model on multiple choice	Show how to preprocess the data and fine-tune a pretrained model on SWAG.
How to fine-tune a model on translation	Show how to preprocess the data and fine-tune a pretrained model on WMT.
How to fine-tune a model on summarization	Show how to preprocess the data and fine-tune a pretrained model on XSUM.
How to fine-tune a pre-trained protein model	See how to tokenize proteins and fine-tune a large pre-trained protein “language” model

Optimum notebooks

🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares.

Notebook	Description
How to quantize a model with ONNX Runtime for text classification	Show how to apply static and dynamic quantization on a model using ONNX Runtime for any GLUE task.
How to quantize a model with Intel Neural Compressor for text classification	Show how to apply static, dynamic and aware training quantization on a model using Intel Neural Compressor (INC) for any GLUE task.
How to fine-tune a model on text classification with ONNX Runtime	Show how to preprocess the data and fine-tune a model on any GLUE task using ONNX Runtime.
How to fine-tune a model on summarization with ONNX Runtime	Show how to preprocess the data and fine-tune a model on XSUM using ONNX Runtime.

Community notebooks:

More notebooks developed by the community are available here.