Honor contributors to models (#11329)

* Honor contributors to models

* Fix typo

* Address review comments

* Add more authors

docs/source/model_doc/albert.rst

@@ -43,7 +43,8 @@ Tips:
   similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
   number of (repeating) layers.
 
-The original code can be found `here <https://github.com/google-research/ALBERT>`__.
+This model was contributed by `lysandre <https://huggingface.co/lysandre>`__. The original code can be found `here
+<https://github.com/google-research/ALBERT>`__.
 
 AlbertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bart.rst

@@ -35,7 +35,8 @@ According to the abstract,
   state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
   of up to 6 ROUGE.
 
-The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`__.
+This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__. The Authors' code can be found `here
+<https://github.com/pytorch/fairseq/tree/master/examples/bart>`__.
 
 
 Examples

docs/source/model_doc/barthez.rst

@@ -16,7 +16,7 @@ BARThez
 Overview
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
+The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
 <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
 2020.
 
@@ -35,7 +35,8 @@ summarization dataset, OrangeSum, that we release with this paper. We also conti
 pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
 provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
 
-The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
+This model was contributed by `moussakam <https://huggingface.co/moussakam>`__. The Authors' code can be found `here
+<https://github.com/moussaKam/BARThez>`__.
 
 
 Examples

docs/source/model_doc/bert.rst

@@ -42,7 +42,8 @@ Tips:
 - BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
   efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
 
-The original code can be found `here <https://github.com/google-research/bert>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/google-research/bert>`__.
 
 BertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bert_japanese.rst

@@ -71,6 +71,8 @@ Tips:
 - This implementation is the same as BERT, except for tokenization method. Refer to the :doc:`documentation of BERT
   <bert>` for more usage examples.
 
+This model was contributed by `cl-tohoku <https://huggingface.co/cl-tohoku>`__.
+
 BertJapaneseTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/bertgeneration.rst

@@ -79,7 +79,8 @@ Tips:
 - For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
   Therefore, no EOS token should be added to the end of the input.
 
-The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The original code can be
+found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
 
 BertGenerationConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bertweet.rst

@@ -54,8 +54,8 @@ Example of use:
     >>> # from transformers import TFAutoModel
     >>> # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
 
-
-The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.
+This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
+<https://github.com/VinAIResearch/BERTweet>`__.
 
 BertweetTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bigbird.rst

@@ -50,7 +50,8 @@ Tips:
 - Current implementation supports only **ITC**.
 - Current implementation doesn't support **num_random_blocks = 0**
 
-The original code can be found `here <https://github.com/google-research/bigbird>`__.
+This model was contributed by `vasudevgupta <https://huggingface.co/vasudevgupta>`__. The original code can be found
+`here <https://github.com/google-research/bigbird>`__.
 
 BigBirdConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/blenderbot.rst

@@ -36,7 +36,8 @@ and code publicly available. Human evaluations show our best models are superior
 dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
 failure cases of our models.*
 
-The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
+This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__. The authors' code can be found `here
+<https://github.com/facebookresearch/ParlAI>`__ .
 
 
 Implementation Notes

docs/source/model_doc/blenderbot_small.rst

@@ -39,7 +39,8 @@ and code publicly available. Human evaluations show our best models are superior
 dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
 failure cases of our models.*
 
-The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The authors' code can be
+found `here <https://github.com/facebookresearch/ParlAI>`__ .
 
 BlenderbotSmallConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bort.rst

@@ -43,4 +43,5 @@ Tips:
   that is sadly not open-sourced yet. It would be very useful for the community, if someone tries to implement the
   algorithm to make BORT fine-tuning work.
 
-The original code can be found `here <https://github.com/alexa/bort/>`__.
+This model was contributed by `stefan-it <https://huggingface.co/stefan-it>`__. The original code can be found `here
+<https://github.com/alexa/bort/>`__.

docs/source/model_doc/camembert.rst

@@ -37,7 +37,8 @@ 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://camembert-model.fr/>`__.
+This model was contributed by `camembert <https://huggingface.co/camembert>`__. The original code can be found `here
+<https://camembert-model.fr/>`__.
 
 CamembertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/convbert.rst

@@ -34,8 +34,10 @@ ConvBERT significantly outperforms BERT and its variants in various downstream t
 fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while
 using less than 1/4 training cost. Code and pre-trained models will be released.*
 
-ConvBERT training tips are similar to those of BERT. The original implementation can be found here:
-https://github.com/yitu-opensource/ConvBert
+ConvBERT training tips are similar to those of BERT.
+
+This model was contributed by `abhishek <https://huggingface.co/abhishek>`__. The original implementation can be found
+here: https://github.com/yitu-opensource/ConvBert
 
 ConvBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/cpm.rst

@@ -33,7 +33,8 @@ language model, which could facilitate several downstream Chinese NLP tasks, suc
 cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many
 NLP tasks in the settings of few-shot (even zero-shot) learning.*
 
-The original implementation can be found here: https://github.com/TsinghuaAI/CPM-Generate
+This model was contributed by `canwenxu <https://huggingface.co/canwenxu>`__. The original implementation can be found
+here: https://github.com/TsinghuaAI/CPM-Generate
 
 Note: We only have a tokenizer here, since the model architecture is the same as GPT-2.
 

docs/source/model_doc/ctrl.rst

@@ -46,7 +46,8 @@ Tips:
   `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
   this argument.
 
-The original code can be found `here <https://github.com/salesforce/ctrl>`__.
+This model was contributed by `keskarnitishr <https://huggingface.co/keskarnitishr>`__. The original code can be found
+`here <https://github.com/salesforce/ctrl>`__.
 
 
 CTRLConfig

docs/source/model_doc/deberta.rst

@@ -38,7 +38,8 @@ the training data performs consistently better on a wide range of NLP tasks, ach
 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
 
 
-The original code can be found `here <https://github.com/microsoft/DeBERTa>`__.
+This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. The original code can be found `here
+<https://github.com/microsoft/DeBERTa>`__.
 
 
 DebertaConfig

docs/source/model_doc/deberta_v2.rst

@@ -58,7 +58,8 @@ New in v2:
 - **900M model & 1.5B model** Two additional model sizes are available: 900M and 1.5B, which significantly improves the
   performance of downstream tasks.
 
-The original code can be found `here <https://github.com/microsoft/DeBERTa>`__.
+This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. The original code can be found `here
+<https://github.com/microsoft/DeBERTa>`__.
 
 
 DebertaV2Config

docs/source/model_doc/deit.rst

@@ -73,6 +73,8 @@ Tips:
   `facebook/deit-base-patch16-384`. Note that one should use :class:`~transformers.DeiTFeatureExtractor` in order to
   prepare images for the model.
 
+This model was contributed by `nielsr <https://huggingface.co/nielsr>`__.
+
 
 DeiTConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/distilbert.rst

@@ -44,7 +44,7 @@ Tips:
 - DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
   necessary though, just let us know if you need this option.
 
-The original code can be found `here
+This model was contributed by `victorsanh <https://huggingface.co/victorsanh>`__. The original code can be found `here
 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
 
 

docs/source/model_doc/dpr.rst

@@ -30,7 +30,8 @@ our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% ab
 retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
 benchmarks.*
 
-The original code can be found `here <https://github.com/facebookresearch/DPR>`__.
+This model was contributed by `lhoestq <https://huggingface.co/lhoestq>`__. The original code can be found `here
+<https://github.com/facebookresearch/DPR>`__.
 
 
 DPRConfig

docs/source/model_doc/electra.rst

@@ -54,7 +54,8 @@ Tips:
   :class:`~transformers.ElectraForPreTraining` model (the classification head will be randomly initialized as it
   doesn't exist in the generator).
 
-The original code can be found `here <https://github.com/google-research/electra>`__.
+This model was contributed by `lysandre <https://huggingface.co/lysandre>`__. The original code can be found `here
+<https://github.com/google-research/electra>`__.
 
 
 ElectraConfig

docs/source/model_doc/flaubert.rst

@@ -35,7 +35,8 @@ time they outperform other pretraining approaches. Different versions of FlauBER
 protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research
 community for further reproducible experiments in French NLP.*
 
-The original code can be found `here <https://github.com/getalp/Flaubert>`__.
+This model was contributed by `formiel <https://huggingface.co/formiel>`__. The original code can be found `here
+<https://github.com/getalp/Flaubert>`__.
 
 
 FlaubertConfig

docs/source/model_doc/fsmt.rst

@@ -34,7 +34,8 @@ data, then decode using noisy channel model reranking. Our submissions are ranke
 human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
 This system improves upon our WMT'18 submission by 4.5 BLEU points.*
 
-The original code can be found here <https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.
+This model was contributed by `stas <https://huggingface.co/stas>`__. The original code can be found here
+<https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.
 
 Implementation Notes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/funnel.rst

@@ -49,7 +49,8 @@ Tips:
   :class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
   :class:`~transformers.FunnelForMultipleChoice`.
 
-The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`__.
+This model was contributed by `sgugger <https://huggingface.co/sgugger>`__. The original code can be found `here
+<https://github.com/laiguokun/Funnel-Transformer>`__.
 
 
 FunnelConfig

docs/source/model_doc/gpt.rst

@@ -45,7 +45,8 @@ Tips:
 `Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by Hugging Face
 showcasing the generative capabilities of several models. GPT is one of them.
 
-The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://github.com/openai/finetune-transformer-lm>`__.
 
 Note:
 

docs/source/model_doc/gpt2.rst

@@ -45,7 +45,8 @@ Tips:
 Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
 different sizes: small, medium, large, xl and a distilled version of the small checkpoint: `distilgpt-2`.
 
-The original code can be found `here <https://openai.com/blog/better-language-models/>`__.
+This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
+<https://openai.com/blog/better-language-models/>`__.
 
 
 GPT2Config

docs/source/model_doc/gpt_neo.rst

@@ -23,6 +23,8 @@ Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like c
 The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
 256 tokens.
 
+This model was contributed by `valhalla <https://huggingface.co/valhalla>`__.
+
 Generation
 _______________________________________________________________________________________________________________________
 

docs/source/model_doc/herbert.rst

@@ -56,7 +56,9 @@ Examples of use:
     >>> model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
 
 
-The original code can be found `here <https://github.com/allegro/HerBERT>`__.
+This model was contributed by `rmroczkowski <https://huggingface.co/rmroczkowski>`__. The original code can be found
+`here <https://github.com/allegro/HerBERT>`__.
+
 
 HerbertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/ibert.rst

@@ -36,8 +36,9 @@ the full-precision baseline. Furthermore, our preliminary implementation of I-BE
 INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has
 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>`__.
 
-The original code can be found `here <https://github.com/kssteven418/I-BERT>`__.
 
 IBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/layoutlm.rst

@@ -80,7 +80,8 @@ occurs. Those can be obtained using the Python Image Library (PIL) library for e
   <https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb>`__.
   It includes an inference part, which shows how to use Google's Tesseract on a new document.
 
-The original code can be found `here <https://github.com/microsoft/unilm/tree/master/layoutlm>`_.
+This model was contributed by `liminghao1630 <https://huggingface.co/liminghao1630>`__. The original code can be found
+`here <https://github.com/microsoft/unilm/tree/master/layoutlm>`_.
 
 
 LayoutLMConfig

docs/source/model_doc/led.rst

@@ -53,6 +53,8 @@ Tips:
 - A notebook showing how to fine-tune LED, can be accessed `here
   <https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing>`__.
 
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__.
+
 
 LEDConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/longformer.rst

@@ -40,7 +40,8 @@ Tips:
   token belongs to which segment. Just separate your segments with the separation token :obj:`tokenizer.sep_token` (or
   :obj:`</s>`).
 
-The Authors' code can be found `here <https://github.com/allenai/longformer>`__.
+This model was contributed by `beltagy <https://huggingface.co/beltagy>`__. The Authors' code can be found `here
+<https://github.com/allenai/longformer>`__.
 
 Longformer Self Attention
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/lxmert.rst

@@ -52,7 +52,8 @@ Tips:
   contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
   both self attention outputs are disregarded.
 
-The original code can be found `here <https://github.com/airsplay/lxmert>`__.
+This model was contributed by `eltoto1219 <https://huggingface.co/eltoto1219>`__. The original code can be found `here
+<https://github.com/airsplay/lxmert>`__.
 
 
 LxmertConfig

docs/source/model_doc/m2m_100.rst

@@ -34,6 +34,8 @@ to create high quality models. Our focus on non-English-Centric models brings ga
 translating between non-English directions while performing competitively to the best single systems of WMT. We
 open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.*
 
+This model was contributed by `valhalla <https://huggingface.co/valhalla>`__.
+
 
 Training and Generation
 _______________________________________________________________________________________________________________________

docs/source/model_doc/marian.rst

@@ -37,6 +37,7 @@ Implementation Notes
     - the model starts generating with :obj:`pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
       :obj:`<s/>`),
 - Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``.
+- This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__.
 
 Naming
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/mbart.rst

@@ -29,7 +29,8 @@ corpora in many languages using the BART objective. mBART is one of the first me
 sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
 on the encoder, decoder, or reconstructing parts of the text.
 
-The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
+This model was contributed by `valhalla <https://huggingface.co/valhalla>`__. The Authors' code can be found `here
+<https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
 
 Training of MBart
 _______________________________________________________________________________________________________________________

docs/source/model_doc/megatron_bert.rst

@@ -77,9 +77,10 @@ The following commands allow you to do the conversion. We assume that the folder
 
     python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_cased.zip
 
-The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
-and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
-approach using "tensor parallel" and "pipeline parallel" techniques.
+This model was contributed by `jdemouth <https://huggingface.co/jdemouth>`__. The original code can be found `here
+<https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU and multi-node implementation of the
+Megatron Language models. In particular, it contains a hybrid model parallel approach using "tensor parallel" and
+"pipeline parallel" techniques.
 
 MegatronBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/megatron_gpt2.rst

@@ -64,7 +64,8 @@ The following command allows you to do the conversion. We assume that the folder
 
     python3 $PATH_TO_TRANSFORMERS/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py megatron_gpt2_345m_v0_0.zip
 
-The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
-and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
-approach using "tensor parallel" and "pipeline parallel" techniques.
+This model was contributed by `jdemouth <https://huggingface.co/jdemouth>`__. The original code can be found `here
+<https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU and multi-node implementation of the
+Megatron Language models. In particular, it contains a hybrid model parallel approach using "tensor parallel" and
+"pipeline parallel" techniques.
 

docs/source/model_doc/mobilebert.rst

@@ -44,7 +44,8 @@ Tips:
   efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
   with a causal language modeling (CLM) objective are better in that regard.
 
-The original code can be found `here <https://github.com/google-research/mobilebert>`__.
+This model was contributed by `vshampor <https://huggingface.co/vshampor>`__. The original code can be found `here
+<https://github.com/google-research/mobilebert>`__.
 
 MobileBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/mt5.rst

@@ -28,7 +28,8 @@ multilingual variant of T5 that was pre-trained on a new Common Crawl-based data
 the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
 benchmarks. All of the code and model checkpoints*
 
-The original code can be found `here <https://github.com/google-research/multilingual-t5>`__.
+This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The original code can be
+found `here <https://github.com/google-research/multilingual-t5>`__.
 
 MT5Config
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~