Adding task guides to resources (#21704)

* added resources: links to task guides that support these models

* minor polishing

* conflict resolved

* link fix

* Update docs/source/en/model_doc/vision-encoder-decoder.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/en/model_doc/switch_transformers.mdx

@@ -32,6 +32,10 @@ Tips:
 This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArtZucker) .
 The original code can be found [here](https://github.com/google/flaxformer/tree/main/flaxformer/architectures/moe).
 
+## Resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
 
 ## SwitchTransformersConfig
 

docs/source/en/model_doc/t5.mdx

@@ -333,6 +333,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb).
 - [`FlaxT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization).
 - [Summarization](https://huggingface.co/course/chapter7/5?fw=pt#summarization) chapter of the 🤗 Hugging Face course.
+- [Summarization task guide](./tasks/summarization)
 
 <PipelineTag pipeline="fill-mask"/>
 
@@ -342,6 +343,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - [`T5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb).
 - [`TFT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).
+- [Translation task guide](./tasks/translation)
 
 <PipelineTag pipeline="question-answering"/>
 

docs/source/en/model_doc/table-transformer.mdx

@@ -47,7 +47,7 @@ found [here](https://github.com/microsoft/table-transformer).
 <PipelineTag pipeline="object-detection"/>
 
 - A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table%20Transformer).
-- It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found [here](https://github.com/microsoft/table-transformer/issues/68). 
+- It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found [here](https://github.com/microsoft/table-transformer/issues/68).
 
 ## TableTransformerConfig
 

docs/source/en/model_doc/tapas.mdx

@@ -569,6 +569,11 @@ Predicted answer: SUM > 87, 53, 69
 
 In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such that the `prev_labels` token types can be overwritten by the predicted `labels` of the previous table-question pair. Again, more info can be found in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for PyTorch) and [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for TensorFlow).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+
 ## TAPAS specific outputs
 [[autodoc]] models.tapas.modeling_tapas.TableQuestionAnsweringOutput
 

docs/source/en/model_doc/timesformer.mdx

@@ -28,6 +28,9 @@ There are many pretrained variants. Select your pretrained model based on the da
 This model was contributed by [fcakyon](https://huggingface.co/fcakyon).
 The original code can be found [here](https://github.com/facebookresearch/TimeSformer).
 
+## Documentation resources
+
+- [Video classification task guide](./tasks/video_classification)
 
 ## TimesformerConfig
 

docs/source/en/model_doc/transfo-xl.mdx

@@ -58,6 +58,10 @@ TransformerXL does **not** work with *torch.nn.DataParallel* due to a bug in PyT
 
 </Tip>
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
 
 ## TransfoXLConfig
 

docs/source/en/model_doc/unispeech-sat.mdx

@@ -44,6 +44,10 @@ Tips:
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
 found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## UniSpeechSatConfig
 

docs/source/en/model_doc/unispeech.mdx

@@ -39,6 +39,10 @@ Tips:
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
 found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## UniSpeechConfig
 

docs/source/en/model_doc/upernet.mdx

@@ -35,6 +35,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - Demo notebooks for UPerNet can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/UPerNet).
 - [`UperNetForSemanticSegmentation`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/semantic-segmentation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb).
+- See also: [Semantic segmentation task guide](./tasks/semantic_segmentation)
 
 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.
 

docs/source/en/model_doc/van.mdx

@@ -39,6 +39,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`VanForImageClassification`] 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).
+- See also: [Image classification task guide](./tasks/image_classification)
 
 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.
 

docs/source/en/model_doc/videomae.mdx

@@ -43,7 +43,7 @@ review it! The resource should ideally demonstrate something new instead of dupl
 **Video classification**
 - [A notebook](https://github.com/huggingface/notebooks/blob/main/examples/video_classification.ipynb) that shows how
 to fine-tune a VideoMAE model on a custom dataset.
-- [Video classification task page](https://huggingface.co/tasks/video-classification)
+- [Video classification task guide](./tasks/video-classification)
 - [A 🤗 Space](https://huggingface.co/spaces/sayakpaul/video-classification-ucf101-subset) showing how to perform inference with a video classification model.
 
 

docs/source/en/model_doc/vit.mdx

@@ -95,6 +95,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`ViTForImageClassification`] 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).
 - A blog on fine-tuning [`ViTForImageClassification`] on a custom dataset can be found [here](https://huggingface.co/blog/fine-tune-vit).
 - More demo notebooks to fine-tune [`ViTForImageClassification`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer).
+- [Image classification task guide](./tasks/image_classification)
 
 Besides that:
 

docs/source/en/model_doc/vit_hybrid.mdx

@@ -44,6 +44,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`ViTHybridForImageClassification`] 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).
+- See also: [Image classification task guide](./tasks/image_classification)
 
 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.
 

docs/source/en/model_doc/vit_msn.mdx

@@ -53,6 +53,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`ViTMSNForImageClassification`] 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).
+- See also: [Image classification task guide](./tasks/image_classification)
 
 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.
 

docs/source/en/model_doc/wav2vec2-conformer.mdx

@@ -33,6 +33,10 @@ an improved word error rate.
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
 The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## Wav2Vec2ConformerConfig
 

docs/source/en/model_doc/wav2vec2.mdx

@@ -43,6 +43,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - A notebook on how to [leverage a pretrained Wav2Vec2 model for emotion classification](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb). 🌎
 - [`Wav2Vec2ForCTC`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb).
+- [Audio classification task guide](./tasks/audio_classification)
 
 <PipelineTag pipeline="automatic-speech-recognition"/>
 
@@ -51,6 +52,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - A blog post on [finetuning XLS-R for Multi-Lingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2).
 - A notebook on how to [create YouTube captions from any video by transcribing audio with Wav2Vec2](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb). 🌎
 - [`Wav2Vec2ForCTC`] is supported by a notebook on [how to finetune a speech recognition model in English](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb), and [how to finetune a speech recognition model in any language](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb).
+- [Automatic speech recognition task guide](./tasks/asr)
 
 🚀 Deploy
 

docs/source/en/model_doc/wavlm.mdx

@@ -44,6 +44,10 @@ Relevant checkpoints can be found under https://huggingface.co/models?other=wavl
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
 found [here](https://github.com/microsoft/unilm/tree/master/wavlm).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## WavLMConfig
 

docs/source/en/model_doc/xglm.mdx

@@ -38,6 +38,10 @@ in social value tasks such as hate speech detection in five languages and find i
 
 This model was contributed by [Suraj](https://huggingface.co/valhalla). The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/xglm).
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## XGLMConfig
 
 [[autodoc]] XGLMConfig

docs/source/en/model_doc/xlm-prophetnet.mdx

@@ -52,6 +52,12 @@ Tips:
 
 - XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE.
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## XLMProphetNetConfig
 
 [[autodoc]] XLMProphetNetConfig

docs/source/en/model_doc/xlm-roberta-xl.mdx

@@ -28,6 +28,14 @@ Tips:
 
 This model was contributed by [Soonhwan-Kwon](https://github.com/Soonhwan-Kwon) and [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## XLMRobertaXLConfig