NorT5 large
The official release of a new generation of NorT5 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:
Encoder-only NorBERT siblings:
Example usage
This model currently needs a custom wrapper from modeling_nort5.py, you should therefore load the model with trust_remote_code=True.
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ltg/nort5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("ltg/nort5-large", trust_remote_code=True)
# MASKED LANGUAGE MODELING
sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er[MASK_0]."
encoding = tokenizer(sentence)
input_tensor = torch.tensor([encoding.input_ids])
output_tensor = model.generate(input_tensor, decoder_start_token_id=7, eos_token_id=8)
tokenizer.decode(output_tensor.squeeze(), skip_special_tokens=True)
# should output: å varme opp
# PREFIX LANGUAGE MODELING
# you need to finetune this model or use `nort5-{size}-lm` model, which is finetuned on prefix language modeling
sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er (Wikipedia) "
encoding = tokenizer(sentence)
input_tensor = torch.tensor([encoding.input_ids])
output_tensor = model.generate(input_tensor, max_new_tokens=50, num_beams=4, do_sample=False)
tokenizer.decode(output_tensor.squeeze())
# 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
The following classes are currently implemented: AutoModel, AutoModelForSeq2SeqLM.
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.",
}