87 lines
3.8 KiB
Markdown
87 lines
3.8 KiB
Markdown
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# BARTpho
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## Overview
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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.
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The abstract from the paper is the following:
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*We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
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sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the "large" architecture and pre-training
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scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
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on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
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outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
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research and applications of generative Vietnamese NLP tasks.*
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This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The original code can be found [here](https://github.com/VinAIResearch/BARTpho).
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## Usage example
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```python
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>>> import torch
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>>> from transformers import AutoModel, AutoTokenizer
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>>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")
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>>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
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>>> line = "Chúng tôi là những nghiên cứu viên."
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>>> input_ids = tokenizer(line, return_tensors="pt")
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>>> with torch.no_grad():
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... features = bartpho(**input_ids) # Models outputs are now tuples
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>>> # With TensorFlow 2.0+:
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>>> from transformers import TFAutoModel
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>>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
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>>> input_ids = tokenizer(line, return_tensors="tf")
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>>> features = bartpho(**input_ids)
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```
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## Usage tips
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- Following mBART, BARTpho uses the "large" architecture of BART with an additional layer-normalization layer on top of
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both the encoder and decoder. Thus, usage examples in the [documentation of BART](bart), when adapting to use
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with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
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For example:
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```python
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>>> from transformers import MBartForConditionalGeneration
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>>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
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>>> TXT = "Chúng tôi là <mask> nghiên cứu viên."
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>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
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>>> logits = bartpho(input_ids).logits
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>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
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>>> probs = logits[0, masked_index].softmax(dim=0)
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>>> values, predictions = probs.topk(5)
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>>> print(tokenizer.decode(predictions).split())
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```
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- This implementation is only for tokenization: "monolingual_vocab_file" consists of Vietnamese-specialized types
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extracted from the pre-trained SentencePiece model "vocab_file" that is available from the multilingual XLM-RoBERTa.
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Other languages, if employing this pre-trained multilingual SentencePiece model "vocab_file" for subword
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segmentation, can reuse BartphoTokenizer with their own language-specialized "monolingual_vocab_file".
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## BartphoTokenizer
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[[autodoc]] BartphoTokenizer
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