#
🤗 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.

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Hugging Face’s notebooks 🤗

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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 |  |
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 |  |

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PyTorch Examples

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Natural Language Processing

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 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 |  |
Reformer | How Reformer pushes the limits of language modeling |  |

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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 |  |
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 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 |  |
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 an image captioning model | Show how to fine-tune BLIP for image captioning on a custom dataset |  |
How to build an image similarity system with Transformers | Show how to build an image similarity system |  |
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 |  |
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 |  |

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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 |  |
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 |  |

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

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Other modalities

Notebook | Description |  |
--- | --- | --- | ---
Probabilistic Time Series Forecasting | See how to train Time Series Transformer on a custom dataset |  |

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Utility notebooks

Notebook | Description |  |
--- | --- | --- | ---
How to export model to ONNX | Highlight how to export and run inference workloads through ONNX |  |

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TensorFlow Examples

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Natural Language Processing

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. |  |

####
Computer Vision

Notebook | Description |  |
--- | --- | --- | ---
How to fine-tune a model on image classification | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification |  |
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 |  |

####
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 |  |

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Utility notebooks

Notebook | Description |  |
--- | --- | --- | ---
How to train TF/Keras models on TPU | See how to train at high speed on Google’s TPU hardware |  |

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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 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.

< > Update on GitHub