NorBERT 3 large

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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. Plese read the paper to learn more details about the model.

Other sizes:

• NorBERT 3 xs (15M)
• NorBERT 3 small (40M)
• NorBERT 3 base (123M)
• NorBERT 3 large (323M)

Generative NorT5 siblings:

• NorT5 xs (32M)
• NorT5 small (88M)
• NorT5 base (228M)
• NorT5 large (808M)

Example usage

This model currently needs a custom wrapper from modeling_norbert.py, you should therefore load the model with trust_remote_code=True.

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-large")
model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-large", trust_remote_code=True)

mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)

# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))

The following classes are currently implemented: AutoModel, AutoModelMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelForQuestionAnswering and AutoModeltForMultipleChoice.

Cite us

@inproceedings{samuel-etal-2023-norbench,
 title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
 author = "Samuel, David and
 Kutuzov, Andrey and
 Touileb, Samia and
 Velldal, Erik and
 {\O}vrelid, Lilja and
 R{\o}nningstad, Egil and
 Sigdel, Elina and
 Palatkina, Anna",
 booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
 month = may,
 year = "2023",
 address = "T{\'o}rshavn, Faroe Islands",
 publisher = "University of Tartu Library",
 url = "https://aclanthology.org/2023.nodalida-1.61",
 pages = "618--633",
 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.",
}

Norwegian masked language models • 11 items • Updated May 13 • 2