Notebooks with examples
🌍 Transformers Notebooks
You can find here a list of the official notebooks provided by BOINC AI.
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.
BOINC AI’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:
A presentation of the various APIs in Transformers
How to run the models of the Transformers library task by task
How to use a tokenizer to preprocess your data
How to use the Trainer to fine-tune a pretrained model
The differences between the tokenizers algorithm
How to use the multilingual models of the library
PyTorch Examples
Natural Language Processing
How to train and use your very own tokenizer
How to easily start using transformers
Show how to preprocess the data and fine-tune a pretrained model on any GLUE task.
Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task.
Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS).
Show how to preprocess the data and fine-tune a pretrained model on SQUAD.
Show how to preprocess the data and fine-tune a pretrained model on SWAG.
Show how to preprocess the data and fine-tune a pretrained model on WMT.
Show how to preprocess the data and fine-tune a pretrained model on XSUM.
Highlight all the steps to effectively train Transformer model on custom data
How to use different decoding methods for language generation with transformers
How to guide language generation with user-provided constraints
How Reformer pushes the limits of language modeling
Computer Vision
Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification
Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification
Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification
Show how to perform zero-shot object detection on images with text queries
Show how to fine-tune BLIP for image captioning on a custom dataset
Show how to build an image similarity system
Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation
Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification
Audio
Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT
Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice
Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting
Biological Sequences
See how to tokenize proteins and fine-tune a large pre-trained protein “language” model
See how to go from protein sequence to a full protein model and PDB file
See how to tokenize DNA and fine-tune a large pre-trained DNA “language” model
Train even larger DNA models in a memory-efficient way
Other modalities
See how to train Time Series Transformer on a custom dataset
Utility notebooks
Highlight how to export and run inference workloads through ONNX
How to benchmark models with transformers
TensorFlow Examples
Natural Language Processing
How to train and use your very own tokenizer
How to easily start using transformers
Show how to preprocess the data and fine-tune a pretrained model on any GLUE task.
Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task.
Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS).
Show how to preprocess the data and fine-tune a pretrained model on SQUAD.
Show how to preprocess the data and fine-tune a pretrained model on SWAG.
Show how to preprocess the data and fine-tune a pretrained model on WMT.
Show how to preprocess the data and fine-tune a pretrained model on XSUM.
Computer Vision
Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification
Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation
Biological Sequences
See how to tokenize proteins and fine-tune a large pre-trained protein “language” model
Utility notebooks
See how to train at high speed on Google’s TPU hardware
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.
Community notebooks:
More notebooks developed by the community are available here.
Last updated
