metadata
language: mn
tags:
- mongolian
- cased
BERT-BASE-MONGOLIAN-CASED
Link to Official Mongolian-BERT repo
Model description
This repository contains pre-trained Mongolian BERT models trained by tugstugi, enod and sharavsambuu. Special thanks to nabar who provided 5x TPUs.
This repository is based on the following open source projects: google-research/bert, huggingface/pytorch-pretrained-BERT and yoheikikuta/bert-japanese.
How to use
from transformers import pipeline, AutoTokenizer, BertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('tugstugi/bert-base-mongolian-cased')
model = BertForMaskedLM.from_pretrained('tugstugi/bert-base-mongolian-cased')
## declare task ##
pipe = pipeline(task="fill-mask", model=model, tokenizer=tokenizer)
## example ##
input_ = 'Миний [MASK] хоол идэх нь тун чухал.'
output_ = pipe(input_)
for i in range(len(output_)):
print(output_[i])
## Output ##
# {'sequence': '[CLS] Миний хувьд хоол идэх нь тун чухал.[SEP]', 'score': 0.8734784722328186, 'token': 95, 'token_str': '▁хувьд'}
# {'sequence': '[CLS] Миний бодлоор хоол идэх нь тун чухал.[SEP]', 'score': 0.09788835793733597, 'token': 6320, 'token_str': '▁бодлоор'}
# {'sequence': '[CLS] Миний хүү хоол идэх нь тун чухал.[SEP]', 'score': 0.0027510314248502254, 'token': 590, 'token_str': '▁хүү'}
# {'sequence': '[CLS] Миний бие хоол идэх нь тун чухал.[SEP]', 'score': 0.0014857524074614048, 'token': 267, 'token_str': '▁бие'}
# {'sequence': '[CLS] Миний охин хоол идэх нь тун чухал.[SEP]', 'score': 0.0013575413031503558, 'token': 1116, 'token_str': '▁охин'}
Training data
Mongolian Wikipedia and the 700 million word Mongolian news data set [Pretraining Procedure]
BibTeX entry and citation info
@misc{mongolian-bert,
author = {Tuguldur, Erdene-Ochir and Gunchinish, Sharavsambuu and Bataa, Enkhbold},
title = {BERT Pretrained Models on Mongolian Datasets},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tugstugi/mongolian-bert/}}
}