
# 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)

```
{"language": ["hu"], "license": "cc-by-nc-4.0", "tags": ["fill-mask", "puli"], "widget": [{"text": "Mes\u00e9lek egy [MASK] az oroszl\u00e1nr\u00f3l."}]}
transformers pytorch megatron-bert fill-mask puli hu license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #megatron-bert #fill-mask #puli #hu #license-cc-by-nc-4.0 #endpoints_compatible #region-us 


# PULI BERT-Large

For further details, see our demo site.

  - Hungarian BERT large model (MegatronBERT)
  - Trained with Megatron-DeepSpeed github
  - Dataset: 36.3 billion words
  - Checkpoint: 1 500 000 steps

## Limitations

- max_seq_length = 1024


If you use this model, please cite the following paper:



## Usage


# PULI BERT-Large

For further details, see our demo site.

  - Hungarian BERT large model (MegatronBERT)
  - Trained with Megatron-DeepSpeed github
  - Dataset: 36.3 billion words
  - Checkpoint: 1 500 000 steps ## Limitations

- max_seq_length = 1024


If you use this model, please cite the following paper: ## Usage
TAGS
#transformers #pytorch #megatron-bert #fill-mask #puli #hu #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
 # PULI BERT-Large

For further details, see our demo site.

  - Hungarian BERT large model (MegatronBERT)
  - Trained with Megatron-DeepSpeed github
  - Dataset: 36.3 billion words
  - Checkpoint: 1 500 000 steps ## Limitations

- max_seq_length = 1024


If you use this model, please cite the following paper: ## Usage
TAGS
#transformers #pytorch #megatron-bert #fill-mask #puli #hu #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
# PULI BERT-Large

For further details, see our demo site.

  - Hungarian BERT large model (MegatronBERT)
  - Trained with Megatron-DeepSpeed github
  - Dataset: 36.3 billion words
  - Checkpoint: 1 500 000 steps## Limitations

- max_seq_length = 1024


If you use this model, please cite the following paper:## Usage