🤗 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 to 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 them:

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

PyTorch Examples

Natural Language Processing

Notebook	Description
Train your tokenizer	How to train and use your very own tokenizer	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
Train your language model	How to easily start using transformers	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
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.	👁 Open in Colab	👁 Open in AWS Studio
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.	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
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).	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
How to fine-tune a model on question answering	Show how to preprocess the data and fine-tune a pretrained model on SQUAD.	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
How to fine-tune a model on multiple choice	Show how to preprocess the data and fine-tune a pretrained model on SWAG.	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
How to fine-tune a model on translation	Show how to preprocess the data and fine-tune a pretrained model on WMT.	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
How to fine-tune a model on summarization	Show how to preprocess the data and fine-tune a pretrained model on XSUM.	👁 Open in Colab	👁 Open in AWS Studio
How to train a language model from scratch	Highlight all the steps to effectively train Transformer model on custom data	👁 Open in Colab	👁 Open in AWS Studio
How to generate text	How to use different decoding methods for language generation with transformers	👁 Open in Colab	👁 Open in AWS Studio
Reformer	How Reformer pushes the limits of language modeling	👁 Open in Colab	👁 Open in AWS Studio

Computer Vision

Notebook	Description
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	👁 Open in Colab	👁 Open in AWS Studio	👁 Open in AMD Dev Cloud
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	👁 Open in Colab	👁 Open in AWS Studio
How to fine-tune a model on image classification (Kornia)	Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification	👁 Open in Colab	👁 Open in AWS Studio
How to perform zero-shot object detection with OWL-ViT	Show how to perform zero-shot object detection on images with text queries	👁 Open in Colab	👁 Open in AWS Studio
How to fine-tune an image captioning model	Show how to fine-tune BLIP for image captioning on a custom dataset	👁 Open in Colab	👁 Open in AWS Studio
How to build an image similarity system with Transformers	Show how to build an image similarity system	👁 Open in Colab	👁 Open in AWS Studio
How to fine-tune a SegFormer model on semantic segmentation	Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation	👁 Open in Colab	👁 Open in AWS Studio
How to fine-tune a VideoMAE model on video classification	Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification	👁 Open in Colab	👁 Open in AWS Studio

Audio

Notebook	Description
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	👁 Open in Colab	👁 Open in AWS Studio
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	👁 Open in Colab	👁 Open in AWS Studio
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	👁 Open in Colab	👁 Open in AWS Studio

Biological Sequences

Notebook	Description
How to fine-tune a pre-trained protein model	See how to tokenize proteins and fine-tune a large pre-trained protein “language” model	👁 Open in Colab	👁 Open in AWS Studio
How to generate protein folds	See how to go from protein sequence to a full protein model and PDB file	👁 Open in Colab	👁 Open in AWS Studio
How to fine-tune a Nucleotide Transformer model	See how to tokenize DNA and fine-tune a large pre-trained DNA “language” model	👁 Open in Colab	👁 Open in AWS Studio
Fine-tune a Nucleotide Transformer model with LoRA	Train even larger DNA models in a memory-efficient way	👁 Open in Colab	👁 Open in AWS Studio

Other modalities

Notebook	Description
Probabilistic Time Series Forecasting	See how to train Time Series Transformer on a custom dataset	👁 Open in Colab	👁 Open in AWS Studio

Utility notebooks

Notebook	Description
How to export model to ONNX	Highlight how to export and run inference workloads through ONNX	👁 Open in Colab	👁 Open in AWS Studio

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 hardware.

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.	👁 Open in Colab	👁 Open in AWS Studio
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.	👁 Open in Colab	👁 Open in AWS Studio
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.	👁 Open in Colab	👁 Open in AWS Studio

Community notebooks

More notebooks developed by the community are available here.

Update on GitHub