From c7cdd7b4fd957b5f93344e608f06fddf28a0e2cb Mon Sep 17 00:00:00 2001 From: Genta Indra Winata Date: Fri, 18 Sep 2020 15:22:32 +0800 Subject: [PATCH] Create README.md for indobert-lite-base-p1 (#7182) --- .../indobert-lite-base-p1/README.md | 60 +++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 model_cards/indobenchmark/indobert-lite-base-p1/README.md diff --git a/model_cards/indobenchmark/indobert-lite-base-p1/README.md b/model_cards/indobenchmark/indobert-lite-base-p1/README.md new file mode 100644 index 00000000000..e219f3be6b8 --- /dev/null +++ b/model_cards/indobenchmark/indobert-lite-base-p1/README.md @@ -0,0 +1,60 @@ +--- +language: indonesian +tags: +- indobert +- indobenchmark +- indonlu +license: mit +inference: false +datasets: +- Indo4B +--- + +# IndoBERT-Lite Base Model (phase1 - uncased) + +[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. + +## All Pre-trained Models + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) | +| `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) | + +## How to use + +### Load model and tokenizer +```python +from transformers import BertTokenizer, AutoModel +tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-base-p1") +model = AutoModel.from_pretrained("indobenchmark/indobert-lite-base-p1") +``` + +### Extract contextual representation +```python +x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1) +print(x, model(x)[0].sum()) +``` + +## Authors + +IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti. + + +## Citation +If you use our work, please cite: + +```bibtex +@inproceedings{wilie2020indonlu, + title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, + author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, + booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, + year={2020} +} +```