---
dataset_info:
- config_name: nno_eng
  features:
  - name: url_nno
    dtype: string
  - name: domain_nno
    dtype: string
  - name: date_nno
    dtype: timestamp[s]
  - name: mimetype_nno
    dtype: string
  - name: fulltext_nno
    list: string
  - name: url_eng
    dtype: string
  - name: domain_eng
    dtype: string
  - name: date_eng
    dtype: timestamp[s]
  - name: mimetype_eng
    dtype: string
  - name: fulltext_eng
    list: string
  splits:
  - name: train
    num_bytes: 642887589
    num_examples: 30901
  - name: validation
    num_bytes: 94753134
    num_examples: 3796
  - name: test
    num_bytes: 62963101
    num_examples: 3795
  download_size: 366544846
  dataset_size: 800603824
- config_name: nob_eng
  features:
  - name: url_nob
    dtype: string
  - name: domain_nob
    dtype: string
  - name: date_nob
    dtype: timestamp[s]
  - name: mimetype_nob
    dtype: string
  - name: fulltext_nob
    list: string
  - name: url_eng
    dtype: string
  - name: domain_eng
    dtype: string
  - name: date_eng
    dtype: timestamp[s]
  - name: mimetype_eng
    dtype: string
  - name: fulltext_eng
    list: string
  splits:
  - name: train
    num_bytes: 2831274825
    num_examples: 157044
  - name: validation
    num_bytes: 717719563
    num_examples: 19720
  - name: test
    num_bytes: 1739432134
    num_examples: 19720
  download_size: 2242424283
  dataset_size: 5288426522
- config_name: nob_nno
  features:
  - name: url_nno
    dtype: string
  - name: domain_nno
    dtype: string
  - name: date_nno
    dtype: timestamp[s]
  - name: mimetype_nno
    dtype: string
  - name: fulltext_nno
    list: string
  - name: url_nob
    dtype: string
  - name: domain_nob
    dtype: string
  - name: date_nob
    dtype: timestamp[s]
  - name: mimetype_nob
    dtype: string
  - name: fulltext_nob
    list: string
  splits:
  - name: train
    num_bytes: 246770050
    num_examples: 31434
  - name: validation
    num_bytes: 64788506
    num_examples: 3928
  - name: test
    num_bytes: 65141193
    num_examples: 3929
  download_size: 137988628
  dataset_size: 376699749
configs:
- config_name: nno_eng
  data_files:
  - split: train
    path: nno_eng/train-*
  - split: validation
    path: nno_eng/validation-*
  - split: test
    path: nno_eng/test-*
- config_name: nob_eng
  data_files:
  - split: train
    path: nob_eng/train-*
  - split: validation
    path: nob_eng/validation-*
  - split: test
    path: nob_eng/test-*
- config_name: nob_nno
  data_files:
  - split: train
    path: nob_nno/train-*
  - split: validation
    path: nob_nno/validation-*
  - split: test
    path: nob_nno/test-*
language:
- en
- nb
- nn
---

# Målfrid parallel 

This dataset contains parallel data for the following languages: Norwegian Bokmål, Norwegian Nynorsk, English. 

## Loading datasets
The dataset is organized by the three language pairs "nob_nno" (Norwegian Bokmål, Norwegian Nynorsk), "nob_eng" (Norwegian Bokmål, English) and "nno_eng" (Norwegian Nynorsk, English).  
Each division has a train, val, test split, and there is no overlap of source domains between the splits.

Load the dataset you want with the `name` arg like this: 
```python
from datasets import load_dataset

ds = load_dataset("NbAiLab/maalfrid_parallel", name="nno_eng")

```


## Source data 
The source data is from the Målfrid project, which involves scraping .no governmental web sites to report language use.  
We combined the following datasets:
- Målfrid 2021 [resource catalogue](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-69/)
- Målfrid 2022 [resource catalogue](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-97/)
- Målfrid 2023 [resource catalogue](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-98/)
- Målfrid 2024 [resource catalogue](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-99/)
- Målfrid 2025 [resource catalogue](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-102/)

## Alignment method
The document pairs were aligned per website with [NbAiLab/nb-sbert-v2-base](https://huggingface.co/NbAiLab/nb-sbert-v2-base) and the sentence-transformers library.  
For English-Norwegian parallel data, the minimun cosine similarity threshold is 0.80, and for Norwegian parallel data it is 0.95. See [source code](https://github.com/Sprakbanken/alignment_eksperimenter) 



## Licence
Norwegian Licence for Open Government Data (NLOD) https://data.norge.no/nlod/en/2.0