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π€ 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 π€ 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 π€
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 |
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. |
Optimum notebooks
π€ Optimum is an extension of π€ 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. |
Community notebooks:
More notebooks developed by the community are available here.