transformers/docs/source/model_doc/bartpho.rst

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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: