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---
language:
- hu
tags:
- fill-mask
license: cc-by-nc-4.0
widget:
- text: "Mesélek egy [MASK] az oroszlánról."
---
# PULI BERT-Large
For further details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
- Hungarian BERT large model (MegatronBERT)
- Trained with Megatron-DeepSpeed [github](https://github.com/microsoft/Megatron-DeepSpeed)
- Dataset: 36.3 billion words
- Checkpoint: 1 500 000 steps
## Limitations
- max_seq_length = 1024
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-puli,
title = {Jönnek a nagyok! BERT-Large, GPT-2 és GPT-3 nyelvmodellek magyar nyelvre},
booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
year = {2023},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Hungary},
author = {Yang, Zijian Győző and Dodé, Réka and Ferenczi, Gergő and Héja, Enikő and Jelencsik-Mátyus, Kinga and Kőrös, Ádám and Laki, László János and Ligeti-Nagy, Noémi and Vadász, Noémi and Váradi, Tamás},
pages = {247--262}
}
```
## Usage
```python
from transformers import BertTokenizer, MegatronBertModel
tokenizer = BertTokenizer.from_pretrained('NYTK/PULI-BERT-Large')
model = MegatronBertModel.from_pretrained('NYTK/PULI-BERT-Large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt', do_lower_case=False)
output = model(**encoded_input)
```
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