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/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