README
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}
}
Contributors
Similar repos
Used in code
README
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}
}