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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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- T5 |
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- NorT5 |
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- Norwegian |
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- encoder-decoder |
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license: cc-by-4.0 |
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--- |
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# NorT5 x-small |
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## Other sizes: |
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- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/nort5-xs) |
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- [NorBERT 3 small (40M)](https://huggingface.co/ltg/nort5-small) |
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- [NorBERT 3 base (123M)](https://huggingface.co/ltg/nort5-base) |
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- [NorBERT 3 large (323M)](https://huggingface.co/ltg/nort5-large) |
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## Example usage |
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This model currently needs a custom wrapper from `modeling_nort5.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 NorT5ForConditionalGeneration |
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tokenizer = AutoTokenizer.from_pretrained("path/to/folder") |
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t5 = NorT5ForConditionalGeneration.from_pretrained("path/to/folder") |
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# MASKED LANGUAGE MODELING |
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sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er[MASK_0]." |
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encoding = tokenizer(sentence) |
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input_tensor = torch.tensor([encoding.input_ids]) |
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output_tensor = model.generate(input_tensor, decoder_start_token_id=7, eos_token_id=8) |
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tokenizer.decode(output_tensor.squeeze(), skip_special_tokens=True) |
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# should output: å varme opp |
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# PREFIX LANGUAGE MODELING |
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# you need to finetune this model or use `nort5-{size}-lm` model, which is finetuned on prefix language modeling |
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sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er (Wikipedia) " |
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encoding = tokenizer(sentence) |
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input_tensor = torch.tensor([encoding.input_ids]) |
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output_tensor = model.generate(input_tensor, max_new_tokens=50, num_beams=4, do_sample=False) |
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tokenizer.decode(output_tensor.squeeze()) |
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# should output: [BOS]ˈoppvarming, det vil si at det skjer en endring i temperaturen i et medium, f.eks. en ovn eller en radiator, slik at den blir varmere eller kaldere, eller at den blir varmere eller kaldere, eller at den blir |
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``` |
