NbAiLab/NCC

from datasets import load_dataset
data = load_dataset("NbAiLab/NCC", streaming=True)

# Clone the training set
git clone https://huggingface.co/datasets/NbAiLab/NCC

# Create one large training file of all shards without unpacking
cat NCC/data/train*.gz > onefile.json.gz

{
"id": "1006205",
"doc_type": "cc100",
"publish_year": 2021,
"lang_fasttext": "nn",
"lang_fasttext_conf": "0.641",
"text": "Eg har ein PLAN! KOS deg og ha ei fin helg"
}

@inproceedings{kummervold-etal-2022-norwegian-colossal,
  title     = {The {N}orwegian colossal corpus: A text corpus for training large {N}orwegian language models},
  author    = {Kummervold, Per E  and
               Wetjen, Freddy    and
               De la Rosa, Javier},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
  year      = {2022},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  url       = {https://aclanthology.org/2022.lrec-1.410},
  pages     = {3852--3860},
  abstract  = {Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.},
}

@inproceedings{kummervold-etal-2021-operationalizing,
  title     = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
  author    = {Kummervold, Per E  and
               De la Rosa, Javier  and
               Wetjen, Freddy  and
               Brygfjeld, Svein Arne},
  booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
  year      = {2021},
  address   = {Reykjavik, Iceland (Online)},
  publisher = {Linköping University Electronic Press, Sweden},
  url       = {https://aclanthology.org/2021.nodalida-main.3},
  pages     = {20--29},
  abstract  = {In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
  The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
  in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other
  languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
  we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.},
}