Automated compatible models list for task guides (#21338)
* initial commit. added tip placeholders and a script * removed unused imports, fixed paths * fixed generated links * make style * split language modeling doc into two: causal language modeling and masked language modeling * added check_task_guides.py to make fix-copies * review feedback addressed
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This guide illustrates how to:
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1. Fine-tune [ViT](https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.
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1. Fine-tune [ViT](model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.
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2. Use your fine-tuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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See the image classification [task page](https://huggingface.co/tasks/image-classification) for more information about its associated models, datasets, and metrics.
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
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<!--End of the generated tip-->
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</Tip>
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