Model description
This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied.
Cite this work as:
Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For
Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea
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