Colab notebooks10/31/2022 ![]() ![]() How to quantize a model with Intel Neural Compressor 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 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. 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 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 benchmark models with transformers ![]() Highlight how to export and run inference workloads through ONNX 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 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 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 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 to: NotebookĪ presentation of the various APIs in Transformers Pull Request so it can be included under the Community notebooks. If you wrote some notebook(s) leveraging □ Transformers and would like 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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