Translation
Transformers
Safetensors
Vietnamese
mbart
text2text-generation
dialect-normalization
vietnamese-dialect
social-media
low-resource
Instructions to use Biu3010/dialect-normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Biu3010/dialect-normalizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Biu3010/dialect-normalizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Biu3010/dialect-normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("Biu3010/dialect-normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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If you encounter language-token issues, set `tokenizer.src_lang = "vi_VN"` and pass `forced_bos_token_id=tokenizer.lang_code_to_id["vi_VN"]` to `model.generate()`.
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## Reference results
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The paper's mBART-large-50 baseline (trained on the `train` split only) achieves **BLEU 0.8166 路 ROUGE-L 0.9384 路 METEOR 0.8925** on ViDia2Std. This full-corpus model cannot be legitimately evaluated on that test set.
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## Citation
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```bibtex
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If you encounter language-token issues, set `tokenizer.src_lang = "vi_VN"` and pass `forced_bos_token_id=tokenizer.lang_code_to_id["vi_VN"]` to `model.generate()`.
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## Citation
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```bibtex
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