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.
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 | | |
| 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 | | |
| 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 | | |
| 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 | | |
| 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 | | |
| Notebook | Description | ||
|---|---|---|---|
| Probabilistic Time Series Forecasting | See how to train Time Series Transformer on a custom dataset | | |
| Notebook | Description | ||
|---|---|---|---|
| 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 | | |
| 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. | | |
| 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 | | |
| 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 | | |
| Notebook | Description | ||
|---|---|---|---|
| How to train TF/Keras models on TPU | See how to train at high speed on Google’s TPU hardware | | |
🤗 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. | | |
More notebooks developed by the community are available here.