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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: apache-2.0 |
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pipeline_tag: text2text-generation |
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--- |
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# NorT5 base |
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<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> |
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The official release of a new generation of NorT5 language models described in paper [**NorBench — A Benchmark for Norwegian Language Models**](https://arxiv.org/abs/2305.03880). Plese read the paper to learn more details about the model. |
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## Other sizes: |
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- [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) |
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- [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) |
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- [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) |
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- [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) |
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## Encoder-only NorBERT siblings: |
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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_nort5.py`, you should therefore load the model with `trust_remote_code=True`. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("ltg/nort5-base", trust_remote_code=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ltg/nort5-base", trust_remote_code=True) |
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# MASKED LANGUAGE MODELING |
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sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er: å[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: ' varme opp et rom.' |
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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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``` |
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The following classes are currently implemented: `AutoModel`, `AutoModelForSeq2SeqLM`. |
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## Cite us |
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```bibtex |
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@inproceedings{samuel-etal-2023-norbench, |
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title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", |
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author = "Samuel, David and |
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Kutuzov, Andrey and |
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Touileb, Samia and |
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Velldal, Erik and |
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{\O}vrelid, Lilja and |
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R{\o}nningstad, Egil and |
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Sigdel, Elina and |
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Palatkina, Anna", |
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booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", |
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month = may, |
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year = "2023", |
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address = "T{\'o}rshavn, Faroe Islands", |
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publisher = "University of Tartu Library", |
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url = "https://aclanthology.org/2023.nodalida-1.61", |
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pages = "618--633", |
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abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", |
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} |
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``` |
