Add BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese (#13788)

* Add the pre-trained BARTpho model

* Add the pre-trained BARTpho model

* Add the pre-trained BARTpho model

* Fix incorrectly sorted and/or formatted imports

* Fix incorrectly sorted and/or formatted style

* Fix check_dummies

* Fix check_dummies

* Fix check_dummies

* Update docs/source/model_doc/bartpho.rst

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/transformers/models/bartpho/__init__.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/transformers/models/bartpho/tokenization_bartpho.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/test_tokenization_bartpho.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/transformers/models/bartpho/tokenization_bartpho.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/test_tokenization_bartpho.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update docs/source/model_doc/bartpho.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/bartpho.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/bartpho/__init__.py

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Add the pre-trained BARTpho model

* Add Tips section in doc and details of monolingual_vocab_file

* Fix conflicts

* Add another tip related to monolingual_vocab_file

* Readd dependency_versions_table.py

* Handle failing checks

* Remove test_list.txt

* Remove md5sum.saved

* Revise Readme.md

Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/bartpho.rst

@@ -0,0 +1,86 @@
+..
+    Copyright 2021 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.
+
+BARTpho
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The BARTpho model was proposed in `BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
+<https://arxiv.org/abs/2109.09701>`__ by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+
+The abstract from the paper is the following:
+
+*We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
+sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the "large" architecture and pre-training
+scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
+on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
+outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
+research and applications of generative Vietnamese NLP tasks.*
+
+Example of use:
+
+.. code-block::
+
+    >>> import torch
+    >>> from transformers import AutoModel, AutoTokenizer
+
+    >>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
+
+    >>> line = "Chúng tôi là những nghiên cứu viên."
+
+    >>> input_ids = tokenizer(line, return_tensors="pt")
+
+    >>> with torch.no_grad():
+    ...     features = bartpho(**input_ids)  # Models outputs are now tuples
+
+    >>> # With TensorFlow 2.0+:
+    >>> from transformers import TFAutoModel
+    >>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
+    >>> input_ids = tokenizer(line, return_tensors="tf")
+    >>> features = bartpho(**input_ids)
+
+Tips:
+
+- Following mBART, BARTpho uses the "large" architecture of BART with an additional layer-normalization layer on top of
+  both the encoder and decoder. Thus, usage examples in the :doc:`documentation of BART <bart>`, when adapting to use
+  with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
+  For example:
+
+.. code-block::
+
+    >>> from transformers import MBartForConditionalGeneration
+    >>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
+    >>> TXT = 'Chúng tôi là <mask> nghiên cứu viên.'
+    >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
+    >>> logits = bartpho(input_ids).logits
+    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
+    >>> probs = logits[0, masked_index].softmax(dim=0)
+    >>> values, predictions = probs.topk(5)
+    >>> print(tokenizer.decode(predictions).split())
+
+- This implementation is only for tokenization: "monolingual_vocab_file" consists of Vietnamese-specialized types
+  extracted from the pre-trained SentencePiece model "vocab_file" that is available from the multilingual XLM-RoBERTa.
+  Other languages, if employing this pre-trained multilingual SentencePiece model "vocab_file" for subword
+  segmentation, can reuse BartphoTokenizer with their own language-specialized "monolingual_vocab_file".
+
+This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
+<https://github.com/VinAIResearch/BARTpho>`__.
+
+BartphoTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartphoTokenizer
+    :members: