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/albert.mdx

@@ -56,6 +56,14 @@ Next sentence prediction is replaced by a sentence ordering prediction: in the i
 This model was contributed by [lysandre](https://huggingface.co/lysandre). This model jax version was contributed by
 [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/ALBERT).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## AlbertConfig
 
 [[autodoc]] AlbertConfig

docs/source/en/model_doc/audio-spectrogram-transformer.mdx

@@ -47,6 +47,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - A notebook illustrating inference with AST for audio classification can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/AST).
 - [`ASTForAudioClassification`] 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).
+- See also: [Audio classification](./tasks/audio_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/bart.mdx

@@ -109,6 +109,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFBartForConditionalGeneration`] 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).
 - [`FlaxBartForConditionalGeneration`] 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"/>
 
@@ -116,12 +117,19 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFBartForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxBartForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb).
 - [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 <PipelineTag pipeline="translation"/>
 
 - A notebook on how to [finetune mBART using Seq2SeqTrainer for Hindi to English translation](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb). 🌎
 - [`BartForConditionalGeneration`] 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).
 - [`TFBartForConditionalGeneration`] 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)
+
+See also:
+- [Text classification task guide](./tasks/sequence_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Causal language modeling task guide](./tasks/language_modeling)
 
 ## BartConfig
 

docs/source/en/model_doc/beit.mdx

@@ -74,6 +74,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- See also: [Image classification task guide](./tasks/image_classification)
+
+**Semantic segmentation**
+- [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/bert.mdx

@@ -72,6 +72,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`BertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
 - [`TFBertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).
 - [`FlaxBertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="token-classification"/>
 
@@ -81,6 +82,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFBertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
 - [`FlaxBertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification).
 - [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Token classification task guide](./tasks/token_classification)
 
 <PipelineTag pipeline="fill-mask"/>
 
@@ -88,6 +90,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFBertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxBertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb).
 - [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 <PipelineTag pipeline="question-answering"/>
 
@@ -95,10 +98,12 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFBertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).
 - [`FlaxBertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering).
 - [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Question answering task guide](./tasks/question_answering)
 
 **Multiple choice**
 - [`BertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb).
 - [`TFBertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb).
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ⚡️ **Inference**
 - A blog post on how to [Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia](https://huggingface.co/blog/bert-inferentia-sagemaker).

docs/source/en/model_doc/big_bird.mdx

@@ -52,6 +52,15 @@ Tips:
 This model was contributed by [vasudevgupta](https://huggingface.co/vasudevgupta). The original code can be found
 [here](https://github.com/google-research/bigbird).
 
+## 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)
+
 ## BigBirdConfig
 
 [[autodoc]] BigBirdConfig

docs/source/en/model_doc/bigbird_pegasus.mdx

@@ -52,6 +52,14 @@ Tips:
 
 The original code can be found [here](https://github.com/google-research/bigbird).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## BigBirdPegasusConfig
 
 [[autodoc]] BigBirdPegasusConfig

docs/source/en/model_doc/biogpt.mdx

@@ -29,6 +29,10 @@ Tips:
 
 This model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/BioGPT).
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## BioGptConfig
 
 [[autodoc]] BioGptConfig

docs/source/en/model_doc/bit.mdx

@@ -37,6 +37,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`BitForImageClassification`] 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/blenderbot-small.mdx

@@ -44,6 +44,12 @@ Tips:
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The authors' code can be
 found [here](https://github.com/facebookresearch/ParlAI).
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## BlenderbotSmallConfig
 
 [[autodoc]] BlenderbotSmallConfig

docs/source/en/model_doc/blenderbot.mdx

@@ -66,6 +66,12 @@ Here is an example of model usage:
 ["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]
 ```
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## BlenderbotConfig
 
 [[autodoc]] BlenderbotConfig

docs/source/en/model_doc/bloom.mdx

@@ -27,13 +27,19 @@ Several smaller versions of the models have been trained on the same dataset. BL
 
 ## Resources
 
-
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLOOM. 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.
 
 <PipelineTag pipeline="text-generation"/>
 
 - [`BloomForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
 
+See also:
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+
+
 ⚡️ Inference
 - A blog on [Optimization story: Bloom inference](https://huggingface.co/blog/bloom-inference-optimization).
 - A blog on [Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate](https://huggingface.co/blog/bloom-inference-pytorch-scripts).

docs/source/en/model_doc/camembert.mdx

@@ -37,6 +37,15 @@ Tips:
 
 This model was contributed by [camembert](https://huggingface.co/camembert). The original code can be found [here](https://camembert-model.fr/).
 
+## 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)
+
 ## CamembertConfig
 
 [[autodoc]] CamembertConfig

docs/source/en/model_doc/canine.mdx

@@ -92,6 +92,13 @@ sequences to the same length):
 >>> sequence_output = outputs.last_hidden_state
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## CANINE specific outputs
 
 [[autodoc]] models.canine.modeling_canine.CanineModelOutputWithPooling

docs/source/en/model_doc/codegen.mdx

@@ -56,6 +56,10 @@ def hello_world():
 hello_world()
 ```
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## CodeGenConfig
 
 [[autodoc]] CodeGenConfig

docs/source/en/model_doc/conditional_detr.mdx

@@ -27,6 +27,9 @@ alt="drawing" width="600"/>
 
 This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The original code can be found [here](https://github.com/Atten4Vis/ConditionalDETR).
 
+## Documentation resources
+
+- [Object detection task guide](./tasks/object_detection)
 
 ## ConditionalDetrConfig
 

docs/source/en/model_doc/convbert.mdx

@@ -45,6 +45,14 @@ ConvBERT training tips are similar to those of BERT.
 This model was contributed by [abhishek](https://huggingface.co/abhishek). The original implementation can be found
 here: https://github.com/yitu-opensource/ConvBert
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## ConvBertConfig
 
 [[autodoc]] ConvBertConfig

docs/source/en/model_doc/convnext.mdx

@@ -47,6 +47,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- 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/ctrl.mdx

@@ -55,6 +55,10 @@ Tips:
 This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
 [here](https://github.com/salesforce/ctrl).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
 
 ## CTRLConfig
 

docs/source/en/model_doc/cvt.mdx

@@ -45,6 +45,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- 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/data2vec.mdx

@@ -54,6 +54,22 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`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).
 
+**Data2VecText 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)
+
+**Data2VecAudio documentation resources**
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
+
+**Data2VecVision documentation resources**
+- [Image classification](./tasks/image_classification)
+- [Semantic segmentation](./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.
 
 ## Data2VecTextConfig

docs/source/en/model_doc/deberta-v2.mdx

@@ -58,6 +58,13 @@ New in v2:
 This model was contributed by [DeBERTa](https://huggingface.co/DeBERTa). This model TF 2.0 implementation was
 contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/DeBERTa).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## DebertaV2Config
 

docs/source/en/model_doc/deberta.mdx

@@ -48,6 +48,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - A blog post on [Supercharged Customer Service with Machine Learning](https://huggingface.co/blog/supercharge-customer-service-with-machine-learning) with DeBERTa.
 - [`DebertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
 - [`TFDebertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="token-classification" />
 
@@ -55,18 +56,21 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFDebertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
 - [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
 - [Byte-Pair Encoding tokenization](https://huggingface.co/course/chapter6/5?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Token classification task guide](./tasks/token_classification)
 
 <PipelineTag pipeline="fill-mask"/>
 
 - [`DebertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
 - [`TFDebertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 <PipelineTag pipeline="question-answering"/>
 
 - [`DebertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).
 - [`TFDebertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).
 - [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Question answering task guide](./tasks/question_answering)
 
 ## DebertaConfig
 

docs/source/en/model_doc/deformable_detr.mdx

@@ -40,6 +40,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- See also: [Object detection task guide](./tasks/object_detection).
 
 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/deit.mdx

@@ -78,6 +78,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- See also: [Image classification task guide](./tasks/image_classification)
 
 Besides that:
 

docs/source/en/model_doc/deta.mdx

@@ -39,6 +39,7 @@ The original code can be found [here](https://github.com/jozhang97/DETA).
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETA.
 
 - Demo notebooks for DETA can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETA).
+- See also: [Object detection task guide](./tasks/object_detection)
 
 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/detr.mdx

@@ -157,6 +157,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- See also: [Object detection task guide](./tasks/object_detection)
 
 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/dinat.mdx

@@ -68,6 +68,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- 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/distilbert.mdx

@@ -75,6 +75,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`DistilBertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
 - [`TFDistilBertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).
 - [`FlaxDistilBertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 
 <PipelineTag pipeline="token-classification"/>
@@ -83,6 +84,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFDistilBertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
 - [`FlaxDistilBertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification).
 - [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Token classification task guide](./tasks/token_classification)
 
 
 <PipelineTag pipeline="fill-mask"/>
@@ -91,6 +93,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFDistilBertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxDistilBertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb).
 - [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 <PipelineTag pipeline="question-answering"/>
 
@@ -98,10 +101,12 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFDistilBertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).
 - [`FlaxDistilBertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering).
 - [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Question answering task guide](./tasks/question_answering)
 
 **Multiple choice**
 - [`DistilBertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb).
 - [`TFDistilBertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb).
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ⚗️ Optimization
 

docs/source/en/model_doc/dpt.mdx

@@ -33,6 +33,7 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 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).
+- 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/efficientformer.mdx

@@ -39,6 +39,9 @@ reach extremely low latency on mobile devices while maintaining high performance
 This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
 The original code can be found [here](https://github.com/snap-research/EfficientFormer).
 
+## Documentation resources
+
+- [Image classification task guide](./tasks/image_classification)
 
 ## EfficientFormerConfig
 

docs/source/en/model_doc/electra.mdx

@@ -64,6 +64,14 @@ Tips:
 
 This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra).
 
+## 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)
 
 ## ElectraConfig
 

docs/source/en/model_doc/ernie.mdx

@@ -47,6 +47,15 @@ You can find all the supported models from huggingface's model hub: [huggingface
 repo: [PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html)
 and [ERNIE](https://github.com/PaddlePaddle/ERNIE/blob/repro).
 
+## 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)
+
 ## ErnieConfig
 
 [[autodoc]] ErnieConfig

docs/source/en/model_doc/ernie_m.mdx

@@ -32,6 +32,13 @@ Tips:
 
 This model was contributed by [Susnato Dhar](https://huggingface.co/susnato). The original code can be found [here](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/ernie_m).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## ErnieMConfig
 
 [[autodoc]] ErnieMConfig

docs/source/en/model_doc/esm.mdx

@@ -86,6 +86,12 @@ help throughout the process!
 The HuggingFace port of ESMFold uses portions of the [openfold](https://github.com/aqlaboratory/openfold) library.
 The `openfold` library is licensed under the Apache License 2.0.
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+
 ## EsmConfig
 
 [[autodoc]] EsmConfig

docs/source/en/model_doc/flaubert.mdx

@@ -46,7 +46,13 @@ This model was contributed by [formiel](https://huggingface.co/formiel). The ori
 Tips:
 - Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).
 
+## Documentation resources
 
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## FlaubertConfig
 

docs/source/en/model_doc/fnet.mdx

@@ -41,6 +41,14 @@ Tips on usage:
 
 This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/google-research/google-research/tree/master/f_net).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## FNetConfig
 
 [[autodoc]] FNetConfig

docs/source/en/model_doc/funnel.mdx

@@ -60,6 +60,14 @@ Tips:
 
 This model was contributed by [sgugger](https://huggingface.co/sgugger). The original code can be found [here](https://github.com/laiguokun/Funnel-Transformer).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 
 ## FunnelConfig
 

docs/source/en/model_doc/git.mdx

@@ -41,6 +41,7 @@ The original code can be found [here](https://github.com/microsoft/GenerativeIma
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.
 
 - Demo notebooks regarding inference + fine-tuning GIT on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GIT).
+- See also: [Causal language modeling task guide](./tasks/language_modeling)
 
 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.

docs/source/en/model_doc/gpt-sw3.mdx

@@ -48,6 +48,12 @@ Example usage:
 Träd är fina för att de är färgstarka. Men ibland är det fint
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## GPTSw3Tokenizer
 
 [[autodoc]] GPTSw3Tokenizer

docs/source/en/model_doc/gpt2.mdx

@@ -73,7 +73,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`GPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling), [text generation example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation), and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
 - [`TFGPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_clmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxGPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb).
-
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
 
 ## GPT2Config
 

docs/source/en/model_doc/gpt_neo.mdx

@@ -50,6 +50,11 @@ The `generate()` method can be used to generate text using GPT Neo model.
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## GPTNeoConfig
 
 [[autodoc]] GPTNeoConfig

docs/source/en/model_doc/gpt_neox.mdx

@@ -57,6 +57,10 @@ The `generate()` method can be used to generate text using GPT Neo model.
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## GPTNeoXConfig
 
 [[autodoc]] GPTNeoXConfig

docs/source/en/model_doc/gpt_neox_japanese.mdx

@@ -47,6 +47,10 @@ The `generate()` method can be used to generate text using GPT NeoX Japanese mod
 人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。
 ```
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## GPTNeoXJapaneseConfig
 
 [[autodoc]] GPTNeoXJapaneseConfig

docs/source/en/model_doc/gptj.mdx

@@ -122,6 +122,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFGPTJForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_clmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxGPTJForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb).
 
+**Documentation resources**
+- [Text classification task guide](./tasks/sequence_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## GPTJConfig
 
 [[autodoc]] GPTJConfig

docs/source/en/model_doc/hubert.mdx

@@ -40,6 +40,10 @@ Tips:
 
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## HubertConfig
 

docs/source/en/model_doc/ibert.mdx

@@ -36,6 +36,13 @@ been open-sourced.*
 
 This model was contributed by [kssteven](https://huggingface.co/kssteven). The original code can be found [here](https://github.com/kssteven418/I-BERT).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/masked_language_modeling)
 
 ## IBertConfig
 

docs/source/en/model_doc/imagegpt.mdx

@@ -77,6 +77,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - 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).
+- 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/layoutlm.mdx

@@ -88,13 +88,20 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - A notebook on how to [fine-tune LayoutLM on the FUNSD dataset with image embeddings](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb).
 
+- See also: [Document question answering task guide](./tasks/document_question_answering)
+
 <PipelineTag pipeline="text-classification" />
 
 - A notebook on how to [fine-tune LayoutLM for sequence classification on the RVL-CDIP dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="token-classification" />
 
 - A notebook on how to [ fine-tune LayoutLM for token classification on the FUNSD dataset](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb).
+- [Token classification task guide](./tasks/token_classification)
+
+**Other resources**
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 🚀 Deploy
 

docs/source/en/model_doc/layoutlmv2.mdx

@@ -266,6 +266,13 @@ print(encoding.keys())
 # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
 ```
 
+## Documentation resources
+
+- [Document question answering task guide](./tasks/document_question_answering)
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+
 ## LayoutLMv2Config
 
 [[autodoc]] LayoutLMv2Config

docs/source/en/model_doc/layoutlmv3.mdx

@@ -52,17 +52,22 @@ LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2
 <PipelineTag pipeline="text-classification"/>
 
 - [`LayoutLMv2ForSequenceClassification`] is supported by this [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/RVL-CDIP/Fine_tuning_LayoutLMv2ForSequenceClassification_on_RVL_CDIP.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="token-classification"/>
 
 - [`LayoutLMv3ForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3) and [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb).
 - A [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Inference_with_LayoutLMv2ForTokenClassification.ipynb) for how to perform inference with [`LayoutLMv2ForTokenClassification`] and a [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/True_inference_with_LayoutLMv2ForTokenClassification_%2B_Gradio_demo.ipynb) for how to perform inference when no labels are available with [`LayoutLMv2ForTokenClassification`].
 - A [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Fine_tuning_LayoutLMv2ForTokenClassification_on_FUNSD_using_HuggingFace_Trainer.ipynb) for how to finetune [`LayoutLMv2ForTokenClassification`] with the 🤗 Trainer.
+- [Token classification task guide](./tasks/token_classification)
 
 <PipelineTag pipeline="question-answering"/>
 
 - [`LayoutLMv2ForQuestionAnswering`] is supported by this [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/DocVQA/Fine_tuning_LayoutLMv2ForQuestionAnswering_on_DocVQA.ipynb).
+- [Question answering task guide](./tasks/question_answering)
 
+**Document question answering**
+- [Document question answering task guide](./tasks/document_question_answering)
 
 ## LayoutLMv3Config
 

docs/source/en/model_doc/led.mdx

@@ -55,6 +55,12 @@ Tips:
 
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
 
 ## LEDConfig
 

docs/source/en/model_doc/levit.mdx

@@ -68,6 +68,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- 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/lilt.mdx

@@ -52,6 +52,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
 
+**Documentation resources**
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+
 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/longformer.mdx

@@ -89,6 +89,14 @@ mlm_labels = tokenizer.encode("This is a sentence from the training data", retur
 loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## LongformerConfig
 
 [[autodoc]] LongformerConfig

docs/source/en/model_doc/longt5.mdx

@@ -86,6 +86,10 @@ The complexity of this mechanism is `O(l(r + l/k))`.
 This model was contributed by [stancld](https://huggingface.co/stancld).
 The original code can be found [here](https://github.com/google-research/longt5).
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
 
 ## LongT5Config
 

docs/source/en/model_doc/luke.mdx

@@ -117,6 +117,13 @@ Example:
 
 This model was contributed by [ikuyamada](https://huggingface.co/ikuyamada) and [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/studio-ousia/luke).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## LukeConfig
 

docs/source/en/model_doc/lxmert.mdx

@@ -51,6 +51,9 @@ Tips:
 
 This model was contributed by [eltoto1219](https://huggingface.co/eltoto1219). The original code can be found [here](https://github.com/airsplay/lxmert).
 
+## Documentation resources
+
+- [Question answering task guide](./tasks/question_answering)
 
 ## LxmertConfig
 

docs/source/en/model_doc/m2m_100.mdx

@@ -91,6 +91,11 @@ loss = model(**model_inputs).loss  # forward pass
 "Life is like a box of chocolate."
 ```
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## M2M100Config
 
 [[autodoc]] M2M100Config

docs/source/en/model_doc/marian.mdx

@@ -161,6 +161,12 @@ Example of translating english to many romance languages, using old-style 2 char
  'Y esto al español']
 ```
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+- [Causal language modeling task guide](./tasks/language_modeling)
+
 ## MarianConfig
 
 [[autodoc]] MarianConfig

docs/source/en/model_doc/markuplm.mdx

@@ -193,6 +193,12 @@ all nodes and xpaths yourself, you can provide them directly to the processor. M
 dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+
 ## MarkupLMConfig
 
 [[autodoc]] MarkupLMConfig

docs/source/en/model_doc/mbart.mdx

@@ -152,6 +152,15 @@ tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
 # => "The Secretary-General of the United Nations says there is no military solution in Syria."
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_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)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## MBartConfig
 
 [[autodoc]] MBartConfig

docs/source/en/model_doc/mctct.mdx

@@ -31,6 +31,9 @@ performance for many languages that also transfers well to LibriSpeech.*
 
 This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl).
 
+## Documentation resources
+
+- [Automatic speech recognition task guide](./tasks/asr)
 
 Tips:
 

docs/source/en/model_doc/megatron-bert.mdx

@@ -78,6 +78,15 @@ This model was contributed by [jdemouth](https://huggingface.co/jdemouth). The o
 Megatron Language models. In particular, it contains a hybrid model parallel approach using "tensor parallel" and
 "pipeline parallel" techniques.
 
+## 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)
+
 ## MegatronBertConfig
 
 [[autodoc]] MegatronBertConfig

docs/source/en/model_doc/mobilebert.mdx

@@ -43,6 +43,14 @@ Tips:
 
 This model was contributed by [vshampor](https://huggingface.co/vshampor). The original code can be found [here](https://github.com/google-research/mobilebert).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## MobileBertConfig
 
 [[autodoc]] MobileBertConfig

docs/source/en/model_doc/mobilenet_v1.mdx

@@ -51,6 +51,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- 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/mobilenet_v2.mdx

@@ -55,6 +55,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <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).
+- See also: [Image classification task guide](./tasks/image_classification)
+
+**Semantic segmentation**
+- [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/mobilevit.mdx

@@ -64,6 +64,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`MobileViTForImageClassification`] 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)
+
+**Semantic segmentation**
+- [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/mpnet.mdx

@@ -40,6 +40,14 @@ Tips:
 
 The original code can be found [here](https://github.com/microsoft/MPNet).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## MPNetConfig
 
 [[autodoc]] MPNetConfig

docs/source/en/model_doc/mt5.mdx

@@ -56,6 +56,11 @@ Google has released the following variants:
 This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
 found [here](https://github.com/google-research/multilingual-t5).
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## MT5Config
 
 [[autodoc]] MT5Config

docs/source/en/model_doc/mvp.mdx

@@ -100,6 +100,15 @@ For lightweight tuning, *i.e.*, fixing the model and only tuning prompts, you ca
 >>> model.set_lightweight_tuning()
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_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)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## MvpConfig
 
 [[autodoc]] MvpConfig

docs/source/en/model_doc/nat.mdx

@@ -63,6 +63,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`NatForImageClassification`] 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/nezha.mdx

@@ -31,6 +31,14 @@ and natural language inference (XNLI).*
 
 This model was contributed by [sijunhe](https://huggingface.co/sijunhe). The original code can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## NezhaConfig
 
 [[autodoc]] NezhaConfig

docs/source/en/model_doc/nllb.mdx

@@ -88,6 +88,11 @@ See example below for a translation from romanian to german:
 UN-Chef sagt, es gibt keine militärische Lösung in Syrien
 ```
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## NllbTokenizer
 
 [[autodoc]] NllbTokenizer

docs/source/en/model_doc/nystromformer.mdx

@@ -33,6 +33,14 @@ favorably relative to other efficient self-attention methods. Our code is availa
 
 This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/Nystromformer).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
+
 ## NystromformerConfig
 
 [[autodoc]] NystromformerConfig

docs/source/en/model_doc/openai-gpt.mdx

@@ -73,6 +73,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="text-classification"/>
 
 - A blog post on [outperforming OpenAI GPT-3 with SetFit for text-classification](https://www.philschmid.de/getting-started-setfit).
+- See also: [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="text-generation"/>
 
@@ -86,6 +87,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [Causal language modeling](https://huggingface.co/course/en/chapter7/6?fw=pt#training-a-causal-language-model-from-scratch) chapter of the 🤗 Hugging Face Course.
 - [`OpenAIGPTLMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling), [text generation example script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-generation/run_generation.py) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
 - [`TFOpenAIGPTLMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_clmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
+- See also: [Causal language modeling task guide](./tasks/language_modeling)
 
 <PipelineTag pipeline="token-classification"/>
 

docs/source/en/model_doc/opt.mdx

@@ -45,7 +45,7 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 <PipelineTag pipeline="text-classification" />
 
-- [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Text classification task guide](sequence_classification.mdx)
 - [`OPTForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
 
 <PipelineTag pipeline="question-answering" />
@@ -56,7 +56,7 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 ⚡️ Inference
 
-- A blog bost on [How 🤗 Accelerate runs very large models thanks to PyTorch](https://huggingface.co/blog/accelerate-large-models) with OPT.
+- A blog post on [How 🤗 Accelerate runs very large models thanks to PyTorch](https://huggingface.co/blog/accelerate-large-models) with OPT.
 
 ## OPTConfig
 

docs/source/en/model_doc/pegasus.mdx

@@ -102,6 +102,12 @@ All the [checkpoints](https://huggingface.co/models?search=pegasus) are fine-tun
 ... )
 ```
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## PegasusConfig
 
 [[autodoc]] PegasusConfig

docs/source/en/model_doc/pegasus_x.mdx

@@ -28,6 +28,11 @@ Tips:
 
 This model was contributed by [zphang](<https://huggingface.co/zphang). The original code can be found [here](https://github.com/google-research/pegasus).
 
+## Documentation resources
+
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## PegasusXConfig
 
 [[autodoc]] PegasusXConfig

docs/source/en/model_doc/perceiver.mdx

@@ -90,6 +90,12 @@ audio classification, video classification, etc.
 
 - Perceiver does **not** work with `torch.nn.DataParallel` due to a bug in PyTorch, see [issue #36035](https://github.com/pytorch/pytorch/issues/36035)
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Image classification task guide](./tasks/image_classification)
+
 ## Perceiver specific outputs
 
 [[autodoc]] models.perceiver.modeling_perceiver.PerceiverModelOutput

docs/source/en/model_doc/plbart.mdx

@@ -78,6 +78,13 @@ it's passed with the `text_target` keyword argument.
 "Returns the maximum value of a b c."
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
+
 ## PLBartConfig
 
 [[autodoc]] PLBartConfig

docs/source/en/model_doc/poolformer.mdx

@@ -48,6 +48,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`PoolFormerForImageClassification`] 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/prophetnet.mdx

@@ -53,6 +53,11 @@ Tips:
 
 The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
+- [Translation task guide](./tasks/translation)
+- [Summarization task guide](./tasks/summarization)
 
 ## ProphetNetConfig
 

docs/source/en/model_doc/qdqbert.mdx

@@ -114,6 +114,15 @@ the instructions in [torch.onnx](https://pytorch.org/docs/stable/onnx.html). Exa
 >>> torch.onnx.export(...)
 ```
 
+## 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)
+
 ## QDQBertConfig
 
 [[autodoc]] QDQBertConfig

docs/source/en/model_doc/reformer.mdx

@@ -151,6 +151,13 @@ input_ids = tokenizer.encode("This is a sentence from the training data", return
 loss = model(input_ids, labels=input_ids)[0]
 ```
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_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)
+
 ## ReformerConfig
 
 [[autodoc]] ReformerConfig

docs/source/en/model_doc/regnet.mdx

@@ -38,6 +38,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`RegNetForImageClassification`] 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/rembert.mdx

@@ -37,6 +37,15 @@ embedding layer. The embeddings are not tied in pre-training, in contrast with B
 embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is
 also similar to the Albert one rather than the BERT one.
 
+## 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)
+
 ## RemBertConfig
 
 [[autodoc]] RemBertConfig

docs/source/en/model_doc/resnet.mdx

@@ -40,6 +40,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`ResNetForImageClassification`] 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/roberta-prelayernorm.mdx

@@ -29,6 +29,14 @@ Tips:
 This model was contributed by [andreasmaden](https://huggingface.co/andreasmaden).
 The original code can be found [here](https://github.com/princeton-nlp/DinkyTrain).
 
+## 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)
 
 ## RobertaPreLayerNormConfig
 

docs/source/en/model_doc/roberta.mdx

@@ -70,6 +70,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`RobertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
 - [`TFRobertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).
 - [`FlaxRobertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb).
+- [Text classification task guide](./tasks/sequence_classification)
 
 <PipelineTag pipeline="token-classification"/>
 
@@ -77,6 +78,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFRobertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
 - [`FlaxRobertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification).
 - [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Token classification task guide](./tasks/token_classification)
 
 <PipelineTag pipeline="fill-mask"/>
 
@@ -85,6 +87,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFRobertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
 - [`FlaxRobertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb).
 - [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
 
 <PipelineTag pipeline="question-answering"/>
 
@@ -93,10 +96,12 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 - [`TFRobertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).
 - [`FlaxRobertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering).
 - [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.
+- [Question answering task guide](./tasks/question_answering)
 
 **Multiple choice**
 - [`RobertaForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb).
 - [`TFRobertaForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb).
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## RobertaConfig
 

docs/source/en/model_doc/roc_bert.mdx

@@ -31,6 +31,15 @@ in the toxic content detection task under human-made attacks.*
 
 This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
 
+## 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)
+
 ## RoCBertConfig
 
 [[autodoc]] RoCBertConfig

docs/source/en/model_doc/roformer.mdx

@@ -37,6 +37,15 @@ Tips:
 
 This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).
 
+## 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)
+
 ## RoFormerConfig
 
 [[autodoc]] RoFormerConfig

docs/source/en/model_doc/segformer.mdx

@@ -91,6 +91,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`SegformerForImageClassification`] 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).
+- [Image classification task guide](./tasks/image_classification)
 
 Semantic segmentation:
 
@@ -98,6 +99,7 @@ Semantic segmentation:
 - A blog on fine-tuning SegFormer on a custom dataset can be found [here](https://huggingface.co/blog/fine-tune-segformer).
 - More demo notebooks on SegFormer (both inference + fine-tuning on a custom dataset) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer).
 - [`TFSegformerForSemanticSegmentation`] is supported by this [example notebook](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb).
+- [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/sew-d.mdx

@@ -36,6 +36,10 @@ Tips:
 
 This model was contributed by [anton-l](https://huggingface.co/anton-l).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## SEWDConfig
 

docs/source/en/model_doc/sew.mdx

@@ -36,6 +36,10 @@ Tips:
 
 This model was contributed by [anton-l](https://huggingface.co/anton-l).
 
+## Documentation resources
+
+- [Audio classification task guide](./tasks/audio_classification)
+- [Automatic speech recognition task guide](./tasks/asr)
 
 ## SEWConfig
 

docs/source/en/model_doc/speech_to_text_2.mdx

@@ -94,6 +94,9 @@ predicted token ids.
 
 See [model hub](https://huggingface.co/models?filter=speech2text2) to look for Speech2Text2 checkpoints.
 
+## Documentation resources
+
+- [Causal language modeling task guide](./tasks/language_modeling)
 
 ## Speech2Text2Config
 

docs/source/en/model_doc/splinter.mdx

@@ -47,6 +47,10 @@ Tips:
 
 This model was contributed by [yuvalkirstain](https://huggingface.co/yuvalkirstain) and [oriram](https://huggingface.co/oriram). The original code can be found [here](https://github.com/oriram/splinter).
 
+## Documentation resources
+
+- [Question answering task guide](./tasks/question-answering)
+
 ## SplinterConfig
 
 [[autodoc]] SplinterConfig

docs/source/en/model_doc/squeezebert.mdx

@@ -46,6 +46,13 @@ Tips:
 
 This model was contributed by [forresti](https://huggingface.co/forresti).
 
+## Documentation resources
+
+- [Text classification task guide](./tasks/sequence_classification)
+- [Token classification task guide](./tasks/token_classification)
+- [Question answering task guide](./tasks/question_answering)
+- [Masked language modeling task guide](./tasks/masked_language_modeling)
+- [Multiple choice task guide](./tasks/multiple_choice)
 
 ## SqueezeBertConfig
 

docs/source/en/model_doc/swin.mdx

@@ -52,6 +52,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`SwinForImageClassification`] 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)
 
 Besides that:
 

docs/source/en/model_doc/swinv2.mdx

@@ -33,6 +33,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 <PipelineTag pipeline="image-classification"/>
 
 - [`Swinv2ForImageClassification`] 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)
 
 Besides that: