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Create README.md #8579
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--- | ||
language: id | ||
tags: | ||
- indobert | ||
- indolem | ||
license: mit | ||
inference: false | ||
datasets: | ||
- 220M words (IndoWiki, IndoWC, News) | ||
--- | ||
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## About | ||
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[IndoBERT](https://arxiv.org/pdf/2011.00677.pdf) 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). | ||
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We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being <b>3.97</b> (similar to English BERT-base). | ||
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This <b>IndoBERT</b> was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. | ||
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| Task | Metric | Bi-LSTM | mBERT | MalayBERT | IndoBERT | | ||
| ---- | ---- | ---- | ---- | ---- | ---- | | ||
| POS Tagging | Acc | 95.4 | <b>96.8</b> | <b>96.8</b> | <b>96.8</b> | | ||
| NER UGM | F1| 70.9 | 71.6 | 73.2 | <b>74.9</b> | | ||
| NER UI | F1 | 82.2 | 82.2 | 87.4 | <b>90.1</b> | | ||
| Dep. Parsing (UD-Indo-GSD) | UAS/LAS | 85.25/80.35 | 86.85/81.78 | 86.99/81.87 | <b>87.12<b/>/<b>82.32</b> | | ||
| Dep. Parsing (UD-Indo-PUD) | UAS/LAS | 84.04/79.01 | <b>90.58</b>/<b>85.44</b> | 88.91/83.56 | 89.23/83.95 | | ||
| Sentiment Analysis | F1 | 71.62 | 76.58 | 82.02 | <b>84.13</b> | | ||
| Summarization | R1/R2/RL | 67.96/61.65/67.24 | 68.40/61.66/67.67 | 68.44/61.38/67.71 | <b>69.93</b>/<b>62.86</b>/<b>69.21</b> | | ||
| Next Tweet Prediction | Acc | 73.6 | 92.4 | 93.1 | <b>93.7</b> | | ||
| Tweet Ordering | Spearman corr. | 0.45 | 0.53 | 0.51 | <b>0.59</b> | | ||
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The paper is published at the 28th COLING 2020. Please refer to https://indolem.github.io for more details about the benchmarks. | ||
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## How to use | ||
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### Load model and tokenizer (tested with transformers==3.5.1) | ||
```python | ||
from transformers import AutoTokenizer, AutoModel | ||
tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased") | ||
model = AutoModel.from_pretrained("indolem/indobert-base-uncased") | ||
``` | ||
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## Citation | ||
If you use our work, please cite: | ||
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```bibtex | ||
@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} | ||
} | ||
``` |
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out of curiosity, why this flag?
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Hi, Julien Thanks for the approval. There is no particular reason for tags, lang as it's stated in our paper.
For the inference, I've previously seen the webpage (automatically) give non-Indonesian text as the input, which I think less suitable for the model.
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Got it, thanks – for the sample input you can folllow this if you want: https://huggingface.co/docs#how-can-i-control-my-models-widgets-example-inputs