Improve documentation coverage for Phobert (#9427)

* first commit

* change phobert to phoBERT as per author in overview

* v3 and v4 both runs on same code hence there is no need to differentiate them
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/index.rst

@@ -388,6 +388,7 @@ TensorFlow and/or Flax.
     model_doc/gpt
     model_doc/gpt2
     model_doc/pegasus
+    model_doc/phobert
     model_doc/prophetnet
     model_doc/rag
     model_doc/reformer

docs/source/model_doc/phobert.rst

+.. 
+    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.
+
+PhoBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The PhoBERT model was proposed in `PhoBERT: Pre-trained language models for Vietnamese
+<https://www.aclweb.org/anthology/2020.findings-emnlp.92.pdf>`__ by Dat Quoc Nguyen, Anh Tuan Nguyen.
+
+The abstract from the paper is the following:
+
+*We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual
+language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent
+best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple
+Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and
+Natural language inference.*
+
+Example of use:
+
+.. code-block::
+
+  import torch
+  from transformers import AutoModel, AutoTokenizer
+
+  phobert = AutoModel.from_pretrained("vinai/phobert-base")
+  tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
+
+  # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
+  line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
+
+  input_ids = torch.tensor([tokenizer.encode(line)])
+
+  with torch.no_grad():
+      features = phobert(input_ids)  # Models outputs are now tuples
+
+  ## With TensorFlow 2.0+:
+  # from transformers import TFAutoModel
+  # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
+
+
+The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
+
+PhobertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PhobertTokenizer
+    :members: 

utils/check_repo.py

@@ -402,8 +402,6 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "BertJapaneseTokenizer",
     "CharacterTokenizer",
     "MecabTokenizer",
-    # Phoebus
-    "PhobertTokenizer",
     # Benchmarks
     "PyTorchBenchmark",
     "PyTorchBenchmarkArguments",