[Docs] Add DialoGPT (#3755)

* add dialoGPT

* update README.md

* fix conflict

* update readme

* add code links to docs

* Update README.md

* Update dialo_gpt2.rst

* Update pretrained_models.rst

* Update docs/source/model_doc/dialo_gpt2.rst

Co-Authored-By: Julien Chaumond <chaumond@gmail.com>

* change filename of dialogpt

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

docs/source/model_doc/albert.rst

@@ -30,6 +30,8 @@ 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>`_.
+
 AlbertConfig
 ~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/bert.rst

@@ -35,6 +35,8 @@ Tips:
   prediction rather than a token prediction. However, averaging over the sequence may yield better results than using
   the [CLS] token.
 
+The original code can be found `here <https://github.com/google-research/bert>`_.
+
 BertConfig
 ~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/camembert.rst

@@ -22,6 +22,8 @@ Tips:
 - This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ 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/>`_.
+
 CamembertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/ctrl.rst

@@ -31,6 +31,8 @@ Tips:
   See `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>`_.
+
 
 CTRLConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/dialogpt.rst

@@ -0,0 +1,29 @@
+DialoGPT
+----------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+DialoGPT was proposed in
+`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.
+It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
+
+The abstract from the paper is the following:
+
+*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). 
+Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
+We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
+The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*
+
+Tips:
+
+- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
+- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.
+ 
+
+DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring <https://huggingface.co/transformers/model_doc/gpt2.html>`_.
+
+The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.

docs/source/model_doc/distilbert.rst

@@ -27,6 +27,8 @@ Tips:
 - DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
 - DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
 
+The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
+
 
 DistilBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/electra.rst

@@ -44,6 +44,8 @@ Tips:
   and the generator may be loaded in the `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>`_.
+
 
 ElectraConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/flaubert.rst

@@ -20,6 +20,8 @@ of the time they outperform other pre-training approaches. Different versions of
 evaluation 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>`_.
+
 
 FlaubertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/gpt.rst

@@ -36,6 +36,9 @@ 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>`_.
+
+
 OpenAIGPTConfig
 ~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/gpt2.rst

@@ -34,6 +34,8 @@ 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/>`_.
+
 
 GPT2Config
 ~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/roberta.rst

@@ -28,6 +28,9 @@ Tips:
 - RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
 - `Camembert <./camembert.html>`__ 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>`_.
+
+
 RobertaConfig
 ~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/t5.rst

@@ -57,6 +57,8 @@ Tips
 - For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention 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>`_.
+
 
 T5Config
 ~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/transformerxl.rst

@@ -30,6 +30,8 @@ Tips:
   The 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>`_.
+
 
 TransfoXLConfig
 ~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/xlm.rst

@@ -30,6 +30,8 @@ Tips:
 - XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the
   `multi-lingual <../multilingual.html>`__ page for more information.
 
+The original code can be found `here <https://github.com/facebookresearch/XLM/>`_.
+
 
 XLMConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/xlmroberta.rst

@@ -28,6 +28,9 @@ Tips:
 - This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ 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>`_.
+
+
 XLMRobertaConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/xlnet.rst

@@ -32,6 +32,8 @@ Tips:
   `target_mapping` inputs to control the attention span and outputs (see examples in `examples/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/>`_.
+
 
 XLNetConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~