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