Cleanup all references to research_projects

Author: Rocketknight1

docs/source/ar/bertology.md

@@ -15,4 +15,4 @@
 - الوصول إلى جميع أوزان الانتباه لكل رأس في BERT/GPT/GPT-2،
 - استرجاع قيم ومشتقات  مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://arxiv.org/abs/1905.10650.
 
-ولمساعدتك على فهم واستخدام هذه الميزات بسهولة، أضفنا مثالًا برمجيًا محددًا: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) أثناء استخراج المعلومات  وتقليص من نموذج تم تدريبه مسبقًا على GLUE.
+ولمساعدتك على فهم واستخدام هذه الميزات بسهولة، أضفنا مثالًا برمجيًا محددًا: [bertology.py](https://github.com/huggingface/transformers-research-projects/tree/main/bertology/run_bertology.py) أثناء استخراج المعلومات  وتقليص من نموذج تم تدريبه مسبقًا على GLUE.

docs/source/ar/run_scripts.md

@@ -2,7 +2,7 @@
 
 بالإضافة إلى دفاتر الملاحظات [notebooks](./notebooks) الخاصة بـ 🤗 Transformers، هناك أيضًا نصوص برمجية توضيحية تُظهر كيفية تدريب نموذج لمهمة باستخدام [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch) أو [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow) أو [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
 
-كما ستجد النصوص البرمجية التي استخدمناها في [مشاريع الأبحاث](https://github.com/huggingface/transformers/tree/main/examples/research_projects) و [الأمثلة القديمة](https://github.com/huggingface/transformers/tree/main/examples/legacy) والتي ساهم بها المجتمع بشكل أساسي. هذه النصوص البرمجية غير مدعومة بشكل نشط وقد تتطلب إصدارًا محددًا من مكتبة 🤗 Transformers والذي من المحتمل أن يكون غير متوافق مع الإصدار الأحدث من المكتبة.
+كما ستجد النصوص البرمجية التي استخدمناها في [مشاريع الأبحاث](https://github.com/huggingface/transformers-research-projects/) و [الأمثلة القديمة](https://github.com/huggingface/transformers/tree/main/examples/legacy) والتي ساهم بها المجتمع بشكل أساسي. هذه النصوص البرمجية غير مدعومة بشكل نشط وقد تتطلب إصدارًا محددًا من مكتبة 🤗 Transformers والذي من المحتمل أن يكون غير متوافق مع الإصدار الأحدث من المكتبة.
 
 لا يُتوقع أن تعمل النصوص البرمجية التوضيحية بشكل مباشر على كل مشكلة، وقد تحتاج إلى تكييف النص البرمجي مع المشكلة التي تحاول حلها. ولمساعدتك في ذلك، تعرض معظم النصوص البرمجية كيفية معالجة البيانات قبل التدريب بشكل كامل، مما يتيح لك تحريرها حسب الحاجة لحالتك الاستخدام.
 

docs/source/de/index.md

@@ -88,7 +88,7 @@ Die Bibliothek enthält derzeit JAX-, PyTorch- und TensorFlow-Implementierungen,
 1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
+1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers-research-projects/tree/main/distillation) and a German version of DistilBERT.
 1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
 1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.

docs/source/de/run_scripts.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 Neben den 🤗 Transformers [notebooks](./notebooks) gibt es auch Beispielskripte, die zeigen, wie man ein Modell für eine Aufgabe mit [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow) oder [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax) trainiert.
 
-Sie werden auch Skripte finden, die wir in unseren [Forschungsprojekten](https://github.com/huggingface/transformers/tree/main/examples/research_projects) und [Legacy-Beispielen](https://github.com/huggingface/transformers/tree/main/examples/legacy) verwendet haben und die größtenteils von der Community stammen. Diese Skripte werden nicht aktiv gepflegt und erfordern eine bestimmte Version von 🤗 Transformers, die höchstwahrscheinlich nicht mit der neuesten Version der Bibliothek kompatibel ist.
+Sie werden auch Skripte finden, die wir in unseren [Forschungsprojekten](https://github.com/huggingface/transformers-research-projects/) und [Legacy-Beispielen](https://github.com/huggingface/transformers/tree/main/examples/legacy) verwendet haben und die größtenteils von der Community stammen. Diese Skripte werden nicht aktiv gepflegt und erfordern eine bestimmte Version von 🤗 Transformers, die höchstwahrscheinlich nicht mit der neuesten Version der Bibliothek kompatibel ist.
 
 Es wird nicht erwartet, dass die Beispielskripte bei jedem Problem sofort funktionieren. Möglicherweise müssen Sie das Skript an das Problem anpassen, das Sie zu lösen versuchen. Um Ihnen dabei zu helfen, legen die meisten Skripte vollständig offen, wie die Daten vorverarbeitet werden, so dass Sie sie nach Bedarf für Ihren Anwendungsfall bearbeiten können.
 

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

docs/source/en/run_scripts.md

@@ -16,7 +16,7 @@ rendered properly in your Markdown viewer.
 
 # Training scripts
 
-Transformers provides many example training scripts for deep learning frameworks (PyTorch, TensorFlow, Flax) and tasks in [transformers/examples](https://github.com/huggingface/transformers/tree/main/examples). There are additional scripts in [transformers/research projects](https://github.com/huggingface/transformers/tree/main/examples/research_projects) and [transformers/legacy](https://github.com/huggingface/transformers/tree/main/examples/legacy), but these aren't actively maintained and requires a specific version of Transformers.
+Transformers provides many example training scripts for deep learning frameworks (PyTorch, TensorFlow, Flax) and tasks in [transformers/examples](https://github.com/huggingface/transformers/tree/main/examples). There are additional scripts in [transformers/research projects](https://github.com/huggingface/transformers-research-projects/) and [transformers/legacy](https://github.com/huggingface/transformers/tree/main/examples/legacy), but these aren't actively maintained and requires a specific version of Transformers.
 
 Example scripts are only examples and you may need to adapt the script to your use-case. To help you with this, most scripts are very transparent in how data is preprocessed, allowing you to edit it as necessary.