![]() How to quantize a model with ONNX Runtime for text classification □ Optimum is an extension of □ Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares. See how to train at high speed on Google’s TPU hardware Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification How to fine-tune a model on image classification TensorFlow Examples Natural Language Processing Notebook How to benchmark models with transformers Highlight how to export and run inference workloads through ONNX See how to train Time Series Transformer on a custom dataset Train even larger DNA models in a memory-efficient way See how to tokenize DNA and fine-tune a large pre-trained DNA “language” modelįine-tune a Nucleotide Transformer model with LoRA How to fine-tune a Nucleotide Transformer model See how to go from protein sequence to a full protein model and PDB file See how to tokenize proteins and fine-tune a large pre-trained protein “language” model How to fine-tune a pre-trained protein model Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting How to fine-tune a model on audio classification Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice How to fine-tune a speech recognition model in any language Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT How to fine-tune a speech recognition model in English Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification How to fine-tune a VideoMAE model on video classification Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation How to fine-tune a SegFormer model on semantic segmentation Show how to build an image similarity system How to build an image similarity system with Transformers Show how to fine-tune BLIP for image captioning on a custom dataset How to fine-tune an image captioning model Show how to perform zero-shot object detection on images with text queries How to perform zero-shot object detection with OWL-ViT Show how to preprocess the data using Kornia 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 Albumentations 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 Torchvision and fine-tune any pretrained Vision model on Image Classification How to fine-tune a model on image classification (Torchvision) How Reformer pushes the limits of language modeling How to guide language generation with user-provided constraints How to use different decoding methods for language generation with transformers Highlight all the steps to effectively train Transformer model on custom data How to train a language model from scratch Show how to preprocess the data and fine-tune a pretrained model on XSUM. How to fine-tune a model on summarization 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 SWAG. ![]() How to fine-tune a model on multiple choice Show how to preprocess the data and fine-tune a pretrained model on SQUAD. How to fine-tune a model on question answering 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 token classification 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 language modeling Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. How to fine-tune a model on text classification How to train and use your very own tokenizer PyTorch Examples Natural Language Processing Notebook ![]() How to use the multilingual models of the library The differences between the tokenizers algorithm How to use the Trainer to fine-tune a pretrained model How to use a tokenizer to preprocess your data How to run the models of the Transformers library task by task 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: NotebookĪ presentation of the various APIs in Transformers Hugging Face’s notebooks □ Documentation notebooks Pull Request so it can be included under the Community notebooks. ![]() If you wrote some notebook(s) leveraging □ Transformers and would like to be listed here, please open a You can find here a list of the official notebooks provided by Hugging Face.Īlso, we would like to list here interesting content created by the community.
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