Add batch of resources (#20647)

* Add resources

* Add more resources

* Add more resources

* Add TAPAS

* Fix pipeline tag

* Fix pipeline tags

* Remove pipeline tag

* Remove depth-estimation tag

* Update docs/source/en/model_doc/segformer.mdx
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Apply suggestion

* Fix segformer
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

README.md

@@ -91,14 +91,21 @@ In Computer Vision:
 - [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
 - [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50)
 - [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
-- [Panoptic Segmentation with DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic)
+- [Panoptic Segmentation with MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [Depth Estimation with DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
 
 In Audio:
 - [Automatic Speech Recognition with Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
 - [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
 
 In Multimodal tasks:
+- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
 - [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Zero-shot Image Classification with CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
+- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
 
 **[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.
 

docs/source/en/model_doc/beit.mdx

@@ -67,6 +67,15 @@ alt="drawing" width="600"/>
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The JAX/FLAX version of this model was
 contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/beit).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`BeitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## BEiT specific outputs
 

docs/source/en/model_doc/bit.mdx

@@ -30,7 +30,6 @@ impact on transfer learning.
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/google-research/big_transfer).
 
-
 ## Resources
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
@@ -45,19 +44,16 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] BitConfig
 
-
 ## BitImageProcessor
 
 [[autodoc]] BitImageProcessor
     - preprocess
 
-
 ## BitModel
 
 [[autodoc]] BitModel
     - forward
 
-
 ## BitForImageClassification
 
 [[autodoc]] BitForImageClassification

docs/source/en/model_doc/clip.mdx

@@ -77,22 +77,13 @@ This model was contributed by [valhalla](https://huggingface.co/valhalla). The o
 
 ## Resources
 
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIP. If you're
-interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
-The resource should ideally demonstrate something new instead of duplicating an existing resource.
-
-<PipelineTag pipeline="text-to-image"/>
-- A blog post on [How to use CLIP to retrieve images from text](https://huggingface.co/blog/fine-tune-clip-rsicd).
-- A blog bost on [How to use CLIP for Japanese text to image generation](https://huggingface.co/blog/japanese-stable-diffusion).
-
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIP.
 
-<PipelineTag pipeline="image-to-text"/>
-- A notebook showing [Video to text matching with CLIP for videos](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/X-CLIP/Video_text_matching_with_X_CLIP.ipynb).
-
-
-<PipelineTag pipeline="zero-shot-classification"/>
-- A notebook showing [Zero shot video classification using CLIP for video](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/X-CLIP/Zero_shot_classify_a_YouTube_video_with_X_CLIP.ipynb).
+- A blog post on [How to fine-tune CLIP on 10,000 image-text pairs](https://huggingface.co/blog/fine-tune-clip-rsicd).
+- CLIP is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text).
 
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
+The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## CLIPConfig
 

docs/source/en/model_doc/convnext.mdx

@@ -40,16 +40,24 @@ alt="drawing" width="600"/>
 This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
 [gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`ConvNextForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## ConvNextConfig
 
 [[autodoc]] ConvNextConfig
 
-
 ## ConvNextFeatureExtractor
 
 [[autodoc]] ConvNextFeatureExtractor
 
-
 ## ConvNextImageProcessor
 
 [[autodoc]] ConvNextImageProcessor
@@ -60,7 +68,6 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlo
 [[autodoc]] ConvNextModel
     - forward
 
-
 ## ConvNextForImageClassification
 
 [[autodoc]] ConvNextForImageClassification

docs/source/en/model_doc/cvt.mdx

@@ -38,6 +38,16 @@ Tips:
 
 This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/microsoft/CvT).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CvT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`CvtForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## CvtConfig
 
 [[autodoc]] CvtConfig

docs/source/en/model_doc/data2vec.mdx

@@ -37,9 +37,6 @@ Tips:
 - For Data2VecAudio, preprocessing is identical to [`Wav2Vec2Model`], including feature extraction
 - For Data2VecText, preprocessing is identical to [`RobertaModel`], including tokenization.
 - For Data2VecVision, preprocessing is identical to [`BeitModel`], including feature extraction.
-- To know how a pre-trained Data2Vec vision model can be fine-tuned on the task of image classification, you can check out
-[this notebook](https://colab.research.google.com/github/sayakpaul/TF-2.0-Hacks/blob/master/data2vec_vision_image_classification.ipynb).
-
 
 This model was contributed by [edugp](https://huggingface.co/edugp) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
 [sayakpaul](https://github.com/sayakpaul) and [Rocketknight1](https://github.com/Rocketknight1) contributed Data2Vec for vision in TensorFlow.
@@ -48,6 +45,17 @@ The original code (for NLP and Speech) can be found [here](https://github.com/py
 The original code for vision can be found [here](https://github.com/facebookresearch/data2vec_vision/tree/main/beit).
 
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Data2Vec.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`Data2VecVisionForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+- To fine-tune [`TFData2VecVisionForImageClassification`] on a custom dataset, see [this notebook](https://colab.research.google.com/github/sayakpaul/TF-2.0-Hacks/blob/master/data2vec_vision_image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## Data2VecTextConfig
 
 [[autodoc]] Data2VecTextConfig

docs/source/en/model_doc/deformable_detr.mdx

@@ -24,7 +24,7 @@ The abstract from the paper is the following:
 Tips:
 
 - One can use [`DeformableDetrImageProcessor`] to prepare images (and optional targets) for the model.
-- Training Deformable DETR is equivalent to training the original [DETR](detr) model. Demo notebooks can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR).
+- Training Deformable DETR is equivalent to training the original [DETR](detr) model. See the [resources](#resources) section below for demo notebooks.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
 alt="drawing" width="600"/>
@@ -33,6 +33,16 @@ alt="drawing" width="600"/>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Deformable DETR.
+
+<PipelineTag pipeline="object-detection"/>
+
+- Demo notebooks regarding inference + fine-tuning on a custom dataset for [`DeformableDetrForObjectDetection`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Deformable-DETR).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## DeformableDetrImageProcessor
 
 [[autodoc]] DeformableDetrImageProcessor
@@ -47,18 +57,15 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
     - pad_and_create_pixel_mask
     - post_process_object_detection
 
-
 ## DeformableDetrConfig
 
 [[autodoc]] DeformableDetrConfig
 
-
 ## DeformableDetrModel
 
 [[autodoc]] DeformableDetrModel
     - forward
 
-
 ## DeformableDetrForObjectDetection
 
 [[autodoc]] DeformableDetrForObjectDetection

docs/source/en/model_doc/deit.mdx

@@ -71,6 +71,19 @@ Tips:
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [amyeroberts](https://huggingface.co/amyeroberts).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeiT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`DeiTForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+Besides that:
+
+- [`DeiTForMaskedImageModeling`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## DeiTConfig
 

docs/source/en/model_doc/detr.mdx

@@ -37,9 +37,6 @@ baselines.*
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/detr).
 
-The quickest way to get started with DETR is by checking the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) (which showcase both inference and
-fine-tuning on custom data).
-
 Here's a TLDR explaining how [`~transformers.DetrForObjectDetection`] works:
 
 First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
@@ -153,6 +150,15 @@ outputs of the model using one of the postprocessing methods of [`~transformers.
 be be provided to either `CocoEvaluator` or `PanopticEvaluator`, which allow you to calculate metrics like
 mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the [original repository](https://github.com/facebookresearch/detr). See the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) for more info regarding evaluation.
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETR.
+
+<PipelineTag pipeline="object-detection"/>
+
+- All example notebooks illustrating fine-tuning [`DetrForObjectDetection`] and [`DetrForSegmentation`] on a custom dataset an be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## DETR specific outputs
 

docs/source/en/model_doc/dinat.mdx

@@ -61,12 +61,20 @@ Taken from the <a href="https://arxiv.org/abs/2209.15001">original paper</a>.</s
 This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
 The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiNAT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`DinatForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## DinatConfig
 
 [[autodoc]] DinatConfig
 
-
 ## DinatModel
 
 [[autodoc]] DinatModel

docs/source/en/model_doc/dit.mdx

@@ -65,3 +65,13 @@ A notebook that illustrates inference for document image classification can be f
 As DiT's architecture is equivalent to that of BEiT, one can refer to [BEiT's documentation page](beit) for all tips, code examples and notebooks.
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/dit).
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`BeitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
\ No newline at end of file

docs/source/en/model_doc/dpt.mdx

@@ -28,37 +28,40 @@ alt="drawing" width="600"/>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/isl-org/DPT).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DPT.
+
+- Demo notebooks for [`DPTForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DPT).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## DPTConfig
 
 [[autodoc]] DPTConfig
 
-
 ## DPTFeatureExtractor
 
 [[autodoc]] DPTFeatureExtractor
     - __call__
     - post_process_semantic_segmentation
 
-
 ## DPTImageProcessor
 
 [[autodoc]] DPTImageProcessor
     - preprocess
     - post_process_semantic_segmentation
 
-
 ## DPTModel
 
 [[autodoc]] DPTModel
     - forward
 
-
 ## DPTForDepthEstimation
 
 [[autodoc]] DPTForDepthEstimation
     - forward
 
-
 ## DPTForSemanticSegmentation
 
 [[autodoc]] DPTForSemanticSegmentation

docs/source/en/model_doc/glpn.mdx

@@ -31,7 +31,6 @@ The abstract from the paper is the following:
 
 Tips:
 
-- A notebook illustrating inference with [`GLPNForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GLPN/GLPN_inference_(depth_estimation).ipynb).
 - One can use [`GLPNImageProcessor`] to prepare images for the model.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/glpn_architecture.jpg"
@@ -41,6 +40,12 @@ alt="drawing" width="600"/>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/vinvino02/GLPDepth).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GLPN.
+
+- Demo notebooks for [`GLPNForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GLPN).
+
 ## GLPNConfig
 
 [[autodoc]] GLPNConfig

docs/source/en/model_doc/groupvit.mdx

@@ -24,11 +24,16 @@ The abstract from the paper is the following:
 Tips:
 
 - You may specify `output_segmentation=True` in the forward of `GroupViTModel` to get the segmentation logits of input texts. 
-- The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference). One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT. 
 
 This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui). The TensorFlow version was contributed by [ariG23498](https://huggingface.co/ariG23498) with the help of [Yih-Dar SHIEH](https://huggingface.co/ydshieh), [Amy Roberts](https://huggingface.co/amyeroberts), and [Joao Gante](https://huggingface.co/joaogante).
 The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GroupViT.
+
+- The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference).
+- One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT. 
 
 ## GroupViTConfig
 

docs/source/en/model_doc/imagegpt.mdx

@@ -38,8 +38,6 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr), based on
 
 Tips:
 
-- Demo notebooks for ImageGPT can be found
-  [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ImageGPT).
 - ImageGPT is almost exactly the same as [GPT-2](gpt2), with the exception that a different activation
   function is used (namely "quick gelu"), and the layer normalization layers don't mean center the inputs. ImageGPT
   also doesn't have tied input- and output embeddings.
@@ -71,6 +69,17 @@ Tips:
 | MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 |
 | MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 |
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ImageGPT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- Demo notebooks for ImageGPT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ImageGPT).
+- [`ImageGPTForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## ImageGPTConfig
 
 [[autodoc]] ImageGPTConfig

docs/source/en/model_doc/levit.mdx

@@ -61,6 +61,15 @@ Tips:
 
 This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/facebookresearch/LeViT).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LeViT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`LevitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## LevitConfig
 

docs/source/en/model_doc/lilt.mdx

@@ -37,7 +37,6 @@ model.push_to_hub("name_of_repo_on_the_hub")
 - When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
 - As [lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) uses the same vocabulary as [LayoutLMv3](layoutlmv3), one can use [`LayoutLMv3TokenizerFast`] to prepare data for the model.
 The same is true for [lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base): one can use [`LayoutXLMTokenizerFast`] for that model.
-- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg"
 alt="drawing" width="600"/>
@@ -47,6 +46,13 @@ alt="drawing" width="600"/>
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/jpwang/lilt).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LiLT.
+
+- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## LiltConfig
 

docs/source/en/model_doc/mobilenet_v1.mdx

@@ -44,6 +44,16 @@ Unsupported features:
 
 This model was contributed by [matthijs](https://huggingface.co/Matthijs). The original code and weights can be found [here](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV1.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`MobileNetV1ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## MobileNetV1Config
 
 [[autodoc]] MobileNetV1Config

docs/source/en/model_doc/mobilenet_v2.mdx

@@ -48,6 +48,16 @@ Unsupported features:
 
 This model was contributed by [matthijs](https://huggingface.co/Matthijs). The original code and weights can be found [here for the main model](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet) and [here for DeepLabV3+](https://github.com/tensorflow/models/tree/master/research/deeplab).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV2.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`MobileNetV2ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## MobileNetV2Config
 
 [[autodoc]] MobileNetV2Config