Remove research projects (#36645)

* Remove research projects

* Add new README to explain where the projects went

* Trigger tests

* Cleanup all references to research_projects

docs/source/en/model_doc/distilbert.md

@@ -49,7 +49,7 @@ demonstrate its capabilities for on-device computations in a proof-of-concept ex
 study.*
 
 This model was contributed by [victorsanh](https://huggingface.co/victorsanh). This model jax version was
-contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation).
+contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers-research-projects/tree/main/distillation).
 
 ## Usage tips
 

docs/source/en/model_doc/layoutlmv3.md

@@ -52,7 +52,7 @@ LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2
 </Tip>
 
 - Demo notebooks for LayoutLMv3 can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3).
-- Demo scripts can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3).
+- Demo scripts can be found [here](https://github.com/huggingface/transformers-research-projects/tree/main/layoutlmv3).
 
 <PipelineTag pipeline="text-classification"/>
 
@@ -61,7 +61,7 @@ LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2
 
 <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).
+- [`LayoutLMv3ForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers-research-projects/tree/main/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)

docs/source/en/model_doc/pegasus.md

@@ -96,7 +96,7 @@ All the [checkpoints](https://huggingface.co/models?search=pegasus) are fine-tun
 
 ## Resources
 
-- [Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh) to fine-tune pegasus
+- [Script](https://github.com/huggingface/transformers-research-projects/tree/main/seq2seq-distillation/finetune_pegasus_xsum.sh) to fine-tune pegasus
   on the XSUM dataset. Data download instructions at [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md).
 - [Causal language modeling task guide](../tasks/language_modeling)
 - [Translation task guide](../tasks/translation)

docs/source/en/model_doc/qdqbert.md

@@ -54,7 +54,7 @@ This model was contributed by [shangz](https://huggingface.co/shangz).
 - QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *google-bert/bert-base-uncased*), and
   perform Quantization Aware Training/Post Training Quantization.
 - A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
-  SQUAD task can be found at [transformers/examples/research_projects/quantization-qdqbert/](examples/research_projects/quantization-qdqbert/).
+  SQUAD task can be found at https://github.com/huggingface/transformers-research-projects/tree/main/quantization-qdqbert.
 
 ### Set default quantizers
 

docs/source/en/model_doc/visual_bert.md

@@ -64,7 +64,7 @@ appropriately for the textual and visual parts.
 The [`BertTokenizer`] is used to encode the text. A custom detector/image processor must be used
 to get the visual embeddings. The following example notebooks show how to use VisualBERT with Detectron-like models:
 
-- [VisualBERT VQA demo notebook](https://github.com/huggingface/transformers/tree/main/examples/research_projects/visual_bert) : This notebook
+- [VisualBERT VQA demo notebook](https://github.com/huggingface/transformers-research-projects/tree/main/visual_bert) : This notebook
   contains an example on VisualBERT VQA.
 
 - [Generate Embeddings for VisualBERT (Colab Notebook)](https://colab.research.google.com/drive/1bLGxKdldwqnMVA5x4neY7-l_8fKGWQYI?usp=sharing) : This notebook contains