indolem/indobert-base-uncased

About

IndoBERT is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:

• Indonesian Wikipedia (74M words)
• news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total)
• an Indonesian Web Corpus (Medved and Suchomel, 2017) (90M words).

We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being 3.97 (similar to English BERT-base).

This IndoBERT was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse.

Task	Metric	Bi-LSTM	mBERT	MalayBERT	IndoBERT
POS Tagging	Acc	95.4	96.8	96.8	96.8
NER UGM	F1	70.9	71.6	73.2	74.9
NER UI	F1	82.2	82.2	87.4	90.1
Dep. Parsing (UD-Indo-GSD)	UAS/LAS	85.25/80.35	86.85/81.78	86.99/81.87	87.12/82.32
Dep. Parsing (UD-Indo-PUD)	UAS/LAS	84.04/79.01	90.58/85.44	88.91/83.56	89.23/83.95
Sentiment Analysis	F1	71.62	76.58	82.02	84.13
Summarization	R1/R2/RL	67.96/61.65/67.24	68.40/61.66/67.67	68.44/61.38/67.71	69.93/62.86/69.21
Next Tweet Prediction	Acc	73.6	92.4	93.1	93.7
Tweet Ordering	Spearman corr.	0.45	0.53	0.51	0.59

The paper is published at the 28th COLING 2020. Please refer to https://indolem.github.io for more details about the benchmarks.

How to use

Load model and tokenizer (tested with transformers==3.5.1)

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased")
model = AutoModel.from_pretrained("indolem/indobert-base-uncased")

Citation

If you use our work, please cite:

@inproceedings{koto2020indolem,
  title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP},
  author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
  booktitle={Proceedings of the 28th COLING},
  year={2020}
}