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Add resources for LayoutLmV2 and reformat documentation resources (#23115)

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* add resources for layoutlmv2

* remove 🌎 from some resources
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Samin Yasar
2023-05-03 19:53:00 +06:00
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docs/source/en/model_doc/layoutlmv2.mdx
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@@ -121,6 +121,28 @@ section below.
In addition, there's LayoutXLM, which is a multilingual version of LayoutLMv2. More information can be found on In addition, there's LayoutXLM, which is a multilingual version of LayoutLMv2. More information can be found on
[LayoutXLM's documentation page](layoutxlm). [LayoutXLM's documentation page](layoutxlm).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv2. 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-classification"/>
- A notebook on how to [finetune LayoutLMv2 for text-classification on RVL-CDIP dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/RVL-CDIP/Fine_tuning_LayoutLMv2ForSequenceClassification_on_RVL_CDIP.ipynb).
- See also: [Text classification task guide](../tasks/sequence_classification)
<PipelineTag pipeline="question-answering"/>
- A notebook on how to [finetune LayoutLMv2 for question-answering on DocVQA dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/DocVQA/Fine_tuning_LayoutLMv2ForQuestionAnswering_on_DocVQA.ipynb).
- See also: [Question answering task guide](../tasks/question_answering)
- See also: [Document question answering task guide](../tasks/document_question_answering)
<PipelineTag pipeline="token-classification"/>
- A notebook on how to [finetune LayoutLMv2 for token-classification on CORD dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/CORD/Fine_tuning_LayoutLMv2ForTokenClassification_on_CORD.ipynb).
- A notebook on how to [finetune LayoutLMv2 for token-classification on FUNSD dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Fine_tuning_LayoutLMv2ForTokenClassification_on_FUNSD_using_HuggingFace_Trainer.ipynb).
- See also: [Token classification task guide](../tasks/token_classification)
## Usage: LayoutLMv2Processor ## Usage: LayoutLMv2Processor
The easiest way to prepare data for the model is to use [`LayoutLMv2Processor`], which internally The easiest way to prepare data for the model is to use [`LayoutLMv2Processor`], which internally
@@ -266,13 +288,6 @@ print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image']) # 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 ## LayoutLMv2Config
[[autodoc]] LayoutLMv2Config [[autodoc]] LayoutLMv2Config