Example of use:
```python
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 documentation of BART, when adapting to use
