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README.md
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MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words).
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As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied.
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```
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Contributed by:
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Aye Mya Hlaing
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Win Pa Pa
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```
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Cite this work as:
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```
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Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For
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Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Korea
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```
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[Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)
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MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words).
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As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied.
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Cite this work as:
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```
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Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For
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Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea
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```
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[Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)
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