Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/vinai/phobert-large/README.md

README.md

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+# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese 
+  
+Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
+
+ - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md)  which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
+ - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
+
+The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744):
+
+    @article{phobert,
+    title     = {{PhoBERT: Pre-trained language models for Vietnamese}},
+    author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},
+    journal   = {Findings of EMNLP},
+    year      = {2020}
+    }
+
+**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
+
+For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!
+
+### Installation <a name="install2"></a>
+ -  Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+        - `git clone https://github.com/huggingface/transformers.git`
+        - `cd transformers`
+        - `pip3 install --upgrade .`
+
+### Pre-trained models <a name="models2"></a>
+
+Model | #params | Arch.  | Pre-training data
+---|---|---|---
+`vinai/phobert-base` | 135M | base | 20GB  of texts
+`vinai/phobert-large` | 370M | large | 20GB  of texts
+
+### Example usage <a name="usage2"></a>
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+phobert = AutoModel.from_pretrained("vinai/phobert-large")
+tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-large")
+
+# 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-large")
+```