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--- |
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language: |
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- 'no' |
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- nb |
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- nn |
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inference: false |
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tags: |
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- BERT |
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- NorBERT |
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- Norwegian |
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- encoder |
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license: cc-by-4.0 |
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--- |
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# NorBERT 3 large |
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## Other sizes: |
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- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) |
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- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) |
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- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) |
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- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) |
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## Example usage |
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This model currently needs a custom wrapper from `modeling_norbert.py`. Then you can use it like this: |
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```python |
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import torch |
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from transformers import AutoTokenizer |
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from modeling_norbert import NorbertForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("path/to/folder") |
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bert = NorbertForMaskedLM.from_pretrained("path/to/folder") |
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mask_id = tokenizer.convert_tokens_to_ids("[MASK]") |
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input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") |
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output_p = bert(**input_text) |
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output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) |
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# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' |
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print(tokenizer.decode(output_text[0].tolist())) |
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``` |
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The following classes are currently implemented: `NorbertForMaskedLM`, `NorbertForSequenceClassification`, `NorbertForTokenClassification`, `NorbertForQuestionAnswering` and `NorbertForMultipleChoice`. |
