
## 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
```

[Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)




{"language": "my", "license": "apache-2.0", "tags": ["MyanBERTa", "Myanmar", "BERT", "RoBERTa"], "datasets": ["MyCorpus", "Web"]}
transformers pytorch roberta fill-mask MyanBERTa Myanmar BERT RoBERTa my dataset:MyCorpus dataset:Web license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #MyanBERTa #Myanmar #BERT #RoBERTa #my #dataset-MyCorpus #dataset-Web #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


## 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:



Download Paper




## 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:



Download Paper
TAGS
#transformers #pytorch #roberta #fill-mask #MyanBERTa #Myanmar #BERT #RoBERTa #my #dataset-MyCorpus #dataset-Web #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## 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:



Download Paper
passage: TAGS
#transformers #pytorch #roberta #fill-mask #MyanBERTa #Myanmar #BERT #RoBERTa #my #dataset-MyCorpus #dataset-Web #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## 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:



Download Paper