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BARTpho

docs/source/en/model_doc/bartpho.md

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This model was released on 2021-09-20 and added to Hugging Face Transformers on 2021-10-18.

<div style="float: right;"> <div class="flex flex-wrap space-x-1"> </div> </div>

BARTpho

BARTpho is a large-scale Vietnamese sequence-to-sequence model. It offers a word-based and syllable-based version. This model is built on the BART large architecture with its denoising pretraining.

You can find all the original checkpoints under the VinAI organization.

[!TIP] This model was contributed by dqnguyen. Check out the right sidebar for examples of how to apply BARTpho to different language tasks.

The example below demonstrates how to summarize text with [Pipeline] or the [AutoModel] class.

<hfoptions id="usage"> <hfoption id="AutoModel">
python
from transformers import AutoTokenizer, BartForConditionalGeneration


tokenizer = AutoTokenizer.from_pretrained(
    "vinai/bartpho-word",
)
model = BartForConditionalGeneration.from_pretrained(
    "vinai/bartpho-word",
    device_map="auto",
)

text = """
Quang tổng hợp hay gọi tắt là quang hợp là quá trình thu nhận và chuyển hóa năng lượng ánh sáng Mặt trời của thực vật,
tảo và một số vi khuẩn để tạo ra hợp chất hữu cơ phục vụ bản thân cũng như làm nguồn thức ăn cho hầu hết các sinh vật
trên Trái Đất. Quang hợp trong thực vật thường liên quan đến chất tố diệp lục màu xanh lá cây và tạo ra oxy như một sản phẩm phụ
"""
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
</hfoption> </hfoptions>

Notes

  • BARTpho uses the large architecture of BART with an additional layer-normalization layer on top of the encoder and decoder. The BART-specific classes should be replaced with the mBART-specific classes.
  • This implementation only handles tokenization through the monolingual_vocab_file file. This is a Vietnamese-specific subset of token types taken from that multilingual vocabulary. If you want to use this tokenizer for another language, replace the monolingual_vocab_file with one specialized for your target language.

BartphoTokenizer

[[autodoc]] BartphoTokenizer