Improve documentation coverage for Bertweet (#9379)

* bertweet docs coverage

* style doc max len 119

* maxlen style rst

* run main() from style_doc

* changed according to  comments

docs/source/index.rst

@@ -356,6 +356,7 @@ TensorFlow and/or Flax.
     model_doc/bart
     model_doc/barthez
     model_doc/bert
+    model_doc/bertweet
     model_doc/bertgeneration
     model_doc/blenderbot
     model_doc/camembert

docs/source/model_doc/bertweet.rst

@@ -0,0 +1,64 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+Bertweet
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The BERTweet model was proposed in `BERTweet: A pre-trained language model for English Tweets
+<https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf>`__ by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.
+
+The abstract from the paper is the following:
+
+*We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having
+the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et
+al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al.,
+2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks:
+Part-of-speech tagging, Named-entity recognition and text classification.*
+
+Example of use:
+
+.. code-block::
+
+  import torch
+  from transformers import AutoModel, AutoTokenizer 
+
+  bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
+
+  # For transformers v4.x+: 
+  tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
+
+  # For transformers v3.x: 
+  # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
+
+  # INPUT TWEET IS ALREADY NORMALIZED!
+  line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
+
+  input_ids = torch.tensor([tokenizer.encode(line)])
+
+  with torch.no_grad():
+      features = bertweet(input_ids)  # Models outputs are now tuples
+
+  ## With TensorFlow 2.0+:
+  # from transformers import TFAutoModel
+  # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
+
+
+The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.
+
+BertweetTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertweetTokenizer
+    :members: 

utils/check_repo.py

@@ -382,8 +382,6 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "BertJapaneseTokenizer",
     "CharacterTokenizer",
     "MecabTokenizer",
-    # Bertweet
-    "BertweetTokenizer",
     # Herbert
     "HerbertTokenizer",
     "HerbertTokenizerFast",