Instructions to use khavitidala/finetuned-indobartv2-id-su with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khavitidala/finetuned-indobartv2-id-su with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("khavitidala/finetuned-indobartv2-id-su") model = AutoModelForSeq2SeqLM.from_pretrained("khavitidala/finetuned-indobartv2-id-su") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"datasets[0]" with value "Indo4B+" is not valid. If possible, use a dataset id from https://hf.co/datasets.
IndoBART-v2 Model fine-tuned version
Fine-tuned version of IndoBART-v2 with machine translation id->su using default hyperparameter from indoBART paper.
by Ryan Abdurohman
IndoBART-v2 Model
IndoBART-v2 is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.
All Pre-trained Models
| Model | #params | Training data |
|---|---|---|
indobenchmark/indobart-v2 |
132M | Indo4B-Plus (26 GB of text) |
Authors
IndoBART was trained and evaluated by Samuel Cahyawijaya*, Genta Indra Winata*, Bryan Wilie*, Karissa Vincentio*, Xiaohong Li*, Adhiguna Kuncoro*, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung
Citation
If you use our work, please cite:
@article{cahyawijaya2021indonlg,
title={IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation},
author={Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu Leylia and others},
journal={arXiv preprint arXiv:2104.08200},
year={2021}
}
- Downloads last month
- 5