creating readme for bert-base-mongolian-uncased (#7440)

model_cards/bayartsogt/bert-base-mongolian-uncased/README.md

+---
+language: "mn"
+tags:
+- bert
+- mongolian
+- uncased
+---
+
+# BERT-BASE-MONGOLIAN-UNCASED
+[Link to Official Mongolian-BERT repo](https://github.com/tugstugi/mongolian-bert)
+
+## Model description
+This repository contains pre-trained Mongolian [BERT](https://arxiv.org/abs/1810.04805) models trained by [tugstugi](https://github.com/tugstugi), [enod](https://github.com/enod) and [sharavsambuu](https://github.com/sharavsambuu).
+Special thanks to [nabar](https://github.com/nabar) who provided 5x TPUs.
+
+This repository is based on the following open source projects: [google-research/bert](https://github.com/google-research/bert/),
+[huggingface/pytorch-pretrained-BERT](https://github.com/huggingface/pytorch-pretrained-BERT) and [yoheikikuta/bert-japanese](https://github.com/yoheikikuta/bert-japanese).
+
+#### How to use
+
+```python
+from transformers import pipeline, AlbertTokenizer, BertForMaskedLM
+
+tokenizer = AlbertTokenizer.from_pretrained('bayartsogt/bert-base-mongolian-uncased')
+model = BertForMaskedLM.from_pretrained('bayartsogt/bert-base-mongolian-uncased')
+
+## 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])
+
+```
+
+
+## Training data
+Mongolian Wikipedia and the 700 million word Mongolian news data set  [[Pretraining Procedure](https://github.com/tugstugi/mongolian-bert#pre-training)]
+
+### BibTeX entry and citation info
+
+```bibtex
+@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/}}
+}
+```