readme: ---
  
  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)
  
  ```
  