Instructions to use LazarusNLP/NusaBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LazarusNLP/NusaBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LazarusNLP/NusaBERT-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/NusaBERT-large") model = AutoModelForMaskedLM.from_pretrained("LazarusNLP/NusaBERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| language: | |
| - ind | |
| - ace | |
| - ban | |
| - bjn | |
| - bug | |
| - gor | |
| - jav | |
| - min | |
| - msa | |
| - nia | |
| - sun | |
| - tet | |
| language_bcp47: | |
| - jv-x-bms | |
| datasets: | |
| - sabilmakbar/indo_wiki | |
| - acul3/KoPI-NLLB | |
| - uonlp/CulturaX | |
| tags: | |
| - bert | |
| # NusaBERT Large | |
| [NusaBERT](https://arxiv.org/abs/2403.01817) Large is a multilingual encoder-based language model based on the [BERT](https://arxiv.org/abs/1810.04805) architecture. We conducted continued pre-training on open-source corpora of [sabilmakbar/indo_wiki](https://huggingface.co/datasets/sabilmakbar/indo_wiki), [acul3/KoPI-NLLB](https://huggingface.co/datasets/acul3/KoPI-NLLB), and [uonlp/CulturaX](https://huggingface.co/datasets/uonlp/CulturaX). On a held-out subset of the corpus, our model achieved: | |
| - `eval_accuracy`: 0.7117 | |
| - `eval_loss`: 1.3268 | |
| - `perplexity`: 3.7690 | |
| This model was trained using the [🤗Transformers](https://github.com/huggingface/transformers) PyTorch framework. All training was done on an NVIDIA H100 GPU. [LazarusNLP/NusaBERT-large](https://huggingface.co/LazarusNLP/NusaBERT-large) is released under Apache 2.0 license. | |
| ## Model Detail | |
| - **Developed by**: [LazarusNLP](https://lazarusnlp.github.io/) | |
| - **Finetuned from**: [IndoBERT Large p1](https://huggingface.co/indobenchmark/indobert-large-p1) | |
| - **Model type**: Encoder-based BERT language model | |
| - **Language(s)**: Indonesian, Acehnese, Balinese, Banjarese, Buginese, Gorontalo, Javanese, Banyumasan, Minangkabau, Malay, Nias, Sundanese, Tetum | |
| - **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html) | |
| - **Contact**: [LazarusNLP](https://lazarusnlp.github.io/) | |
| ## Use in 🤗Transformers | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| model_checkpoint = "LazarusNLP/NusaBERT-large" | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| model = AutoModelForMaskedLM.from_pretrained(model_checkpoint) | |
| ``` | |
| ## Training Datasets | |
| Around 16B tokens from the following corpora were used during pre-training. | |
| - [Indonesian Wikipedia Data Repository](https://huggingface.co/datasets/sabilmakbar/indo_wiki) | |
| - [KoPI-NLLB (Korpus Perayapan Indonesia)](https://huggingface.co/datasets/acul3/KoPI-NLLB) | |
| - [Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages](https://huggingface.co/datasets/uonlp/CulturaX) | |
| ## Training Hyperparameters | |
| The following hyperparameters were used during training: | |
| - `learning_rate`: 3e-05 | |
| - `train_batch_size`: 256 | |
| - `eval_batch_size`: 256 | |
| - `seed`: 42 | |
| - `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08` | |
| - `lr_scheduler_type`: linear | |
| - `lr_scheduler_warmup_steps`: 24000 | |
| - `training_steps`: 500000 | |
| ### Framework versions | |
| - Transformers 4.38.1 | |
| - Pytorch 2.2.0+cu118 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.15.2 | |
| ## Credits | |
| NusaBERT Large is developed with love by: | |
| <div style="display: flex;"> | |
| <a href="https://github.com/anantoj"> | |
| <img src="https://github.com/anantoj.png" alt="GitHub Profile" style="border-radius: 50%;width: 64px;margin:0 4px;"> | |
| </a> | |
| <a href="https://github.com/DavidSamuell"> | |
| <img src="https://github.com/DavidSamuell.png" alt="GitHub Profile" style="border-radius: 50%;width: 64px;margin:0 4px;"> | |
| </a> | |
| <a href="https://github.com/stevenlimcorn"> | |
| <img src="https://github.com/stevenlimcorn.png" alt="GitHub Profile" style="border-radius: 50%;width: 64px;margin:0 4px;"> | |
| </a> | |
| <a href="https://github.com/w11wo"> | |
| <img src="https://github.com/w11wo.png" alt="GitHub Profile" style="border-radius: 50%;width: 64px;margin:0 4px;"> | |
| </a> | |
| </div> | |
| ## Citation | |
| ```bib | |
| @misc{wongso2024nusabert, | |
| title={NusaBERT: Teaching IndoBERT to be Multilingual and Multicultural}, | |
| author={Wilson Wongso and David Samuel Setiawan and Steven Limcorn and Ananto Joyoadikusumo}, | |
| year={2024}, | |
| eprint={2403.01817}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` |