README.md

---
library_name: transformers
language:
- udm
---

# Zerpal-mBERT-tokenizer

## How to use

You can use this model directly with a pipeline for masked language modeling:

```py
from transformers import pipeline

unmasker = pipeline('fill-mask', model='udmurtNLP/zerpal-mbert', tokenizer='udmurtNLP/zerpal-mbert-tokenizer')

unmasker("Ӟечбур! Мынам нимы [MASK].")
```

Here is how to use this model to get the features of a given text in PyTorch:
```py
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('udmurtNLP/zerpal-mbert-tokenizer')
model = BertModel.from_pretrained("udmurtNLP/zerpal-mbert")
text = "Яратон, яратон, мар меда сыӵе тон?"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```