Honor contributors to models (#11329)

* Honor contributors to models

* Fix typo

* Address review comments

* Add more authors

docs/source/model_doc/pegasus.rst

@@ -31,7 +31,8 @@ According to the abstract,
   extractive summary.
 - Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.
 
-The Authors' code can be found `here <https://github.com/google-research/pegasus>`__.
+This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__. The Authors' code can be found `here
+<https://github.com/google-research/pegasus>`__.
 
 
 Checkpoints

docs/source/model_doc/phobert.rst

@@ -50,7 +50,7 @@ Example of use:
     >>> # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
 
 
-The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
+    This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
 
 PhobertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/rag.rst

@@ -43,6 +43,7 @@ outperforming parametric seq2seq models and task-specific retrieve-and-extract a
 tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art
 parametric-only seq2seq baseline.*
 
+This model was contributed by `ola13 <https://huggingface.co/ola13>`__.
 
 
 RagConfig

docs/source/model_doc/reformer.rst

@@ -32,7 +32,8 @@ layers instead of the standard residuals, which allows storing activations only
 N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models
 while being much more memory-efficient and much faster on long sequences.*
 
-The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`__.
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
+found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`__.
 
 Axial Positional Encodings
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/retribert.rst

@@ -20,8 +20,8 @@ The RetriBERT model was proposed in the blog post `Explain Anything Like I'm Fiv
 Question Answering <https://yjernite.github.io/lfqa.html>`__. RetriBERT is a small model that uses either a single or
 pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
 
-Code to train and use the model can be found `here
-<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
+This model was contributed by `yjernite <https://huggingface.co/yjernite>`__. Code to train and use the model can be
+found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
 
 
 RetriBertConfig

docs/source/model_doc/roberta.rst

@@ -44,7 +44,8 @@ Tips:
   separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`</s>`)
 - :doc:`CamemBERT <camembert>` is a wrapper around RoBERTa. Refer to this page for usage examples.
 
-The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
+This model was contributed by `julien-c <https://huggingface.co/julien-c>`__. The original code can be found `here
+<https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
 
 
 RobertaConfig

docs/source/model_doc/speech_to_text.rst

@@ -25,7 +25,8 @@ transcripts/translations autoregressively. Speech2Text has been fine-tuned on se
 `LibriSpeech <http://www.openslr.org/12>`__, `CoVoST 2 <https://github.com/facebookresearch/covost>`__, `MuST-C
 <https://ict.fbk.eu/must-c/>`__.
 
-The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text>`__.
+This model was contributed by `valhalla <https://huggingface.co/valhalla>`__. The original code can be found `here
+<https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text>`__.
 
 
 Inference

docs/source/model_doc/squeezebert.rst

@@ -47,6 +47,9 @@ Tips:
 - For best results when finetuning on sequence classification tasks, it is recommended to start with the
   `squeezebert/squeezebert-mnli-headless` checkpoint.
 
+This model was contributed by `forresti <https://huggingface.co/forresti>`__.
+
+
 SqueezeBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/t5.rst

@@ -48,7 +48,8 @@ Tips:
   layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar embeddings.
   Encoder input padding can be done on the left and on the right.
 
-The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/google-research/text-to-text-transfer-transformer>`__.
 
 Training
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/tapas.rst

@@ -49,7 +49,8 @@ entailment (a binary classification task). For more details, see their follow-up
 intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
 Syrine Krichene and Thomas Müller.
 
-The original code can be found `here <https://github.com/google-research/tapas>`__.
+This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
+<https://github.com/google-research/tapas>`__.
 
 Tips:
 

docs/source/model_doc/transformerxl.rst

@@ -41,7 +41,8 @@ Tips:
   original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
 - Transformer-XL is one of the few models that has no sequence length limit.
 
-The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/kimiyoung/transformer-xl>`__.
 
 
 TransfoXLConfig

docs/source/model_doc/vit.rst

@@ -67,7 +67,8 @@ Tips:
   improvement of 2% to training from scratch, but still 4% behind supervised pre-training.
 
 
-The original code (written in JAX) can be found `here <https://github.com/google-research/vision_transformer>`__.
+This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code (written in JAX) can be
+found `here <https://github.com/google-research/vision_transformer>`__.
 
 Note that we converted the weights from Ross Wightman's `timm library
 <https://github.com/rwightman/pytorch-image-models>`__, who already converted the weights from JAX to PyTorch. Credits

docs/source/model_doc/wav2vec2.rst

@@ -36,6 +36,8 @@ Tips:
 - Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
   using :class:`~transformers.Wav2Vec2CTCTokenizer`.
 
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__.
+
 
 Wav2Vec2Config
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/xlm.rst

@@ -42,7 +42,8 @@ Tips:
 - XLM has multilingual checkpoints which leverage a specific :obj:`lang` parameter. Check out the :doc:`multi-lingual
   <../multilingual>` page for more information.
 
-The original code can be found `here <https://github.com/facebookresearch/XLM/>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/facebookresearch/XLM/>`__.
 
 
 XLMConfig

docs/source/model_doc/xlmroberta.rst

@@ -44,7 +44,8 @@ Tips:
 - This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage examples
   as well as the information relative to the inputs and outputs.
 
-The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
+This model was contributed by `stefan-it <https://huggingface.co/stefan-it>`__. The original code can be found `here
+<https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
 
 
 XLMRobertaConfig

docs/source/model_doc/xlnet.rst

@@ -44,7 +44,8 @@ Tips:
   `examples/text-generation/run_generation.py`)
 - XLNet is one of the few models that has no sequence length limit.
 
-The original code can be found `here <https://github.com/zihangdai/xlnet/>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/zihangdai/xlnet/>`__.
 
 
 XLNetConfig

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/{{cookiecutter.lowercase_modelname}}.rst

@@ -27,6 +27,10 @@ Tips:
 
 <INSERT TIPS ABOUT MODEL HERE>
 
+This model was contributed by `<INSERT YOUR HF USERNAME HERE> 
+<https://huggingface.co/<INSERT YOUR HF USERNAME HERE>>`__. The original code can be found `here 
+<<INSERT LINK TO GITHUB REPO HERE>>`__.
+
 {{cookiecutter.camelcase_modelname}}Config
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~