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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: apache-2.0 |
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--- |
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# NorBERT 3 small |
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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 NorBERT language models described in paper [**NorBench — A Benchmark for Norwegian Language Models**](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. |
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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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## Generative NorT5 siblings: |
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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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## Example usage |
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This model currently needs a custom wrapper from `modeling_norbert.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, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-small") |
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model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-small", trust_remote_code=True) |
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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 = model(**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: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. |
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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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``` |
