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---
dataset_info:
features:
- name: id
dtype: string
- name: nb
dtype: string
- name: nn
dtype: string
- name: nb_license
dtype: string
- name: nn_license
dtype: string
- name: nb_creators
list:
- name: type
dtype: string
- name: name
dtype: string
- name: nn_creators
list:
- name: type
dtype: string
- name: name
dtype: string
splits:
- name: train
num_bytes: 92732804
num_examples: 189652
- name: validation
num_bytes: 511425
num_examples: 1026
- name: test
num_bytes: 557509
num_examples: 1017
download_size: 39895954
dataset_size: 93801738
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# NDLA Parallel Paragraphs
## Dataset Summary
This dataset is derived from articles provided through the [NDLA (Norwegian Digital Learning Arena)](https://ndla.no) API. It consists of aligned paragraph-level translations between Norwegian Bokmål and Norwegian Nynorsk. The data is sourced from educational articles designed for upper secondary education and has been collected via the official [NDLA Article API](https://api.ndla.no/article-api/v2/articles).
The dataset is intended for machine translation, language modeling, and linguistic research focused on the Norwegian language. Paragraphs have been aligned between Bokmål and Nynorsk versions of the same article. Basic filtering has been applied to remove malformed or empty entries. The dataset is formatted in JSON Lines (`.jsonl`) and is compatible with Hugging Face's `datasets` library.
---
## Dataset Structure
### Data Fields
Each entry in the dataset is a JSON object with the following fields:
- `id`: Unique identifier for the translation pair.
- `article_id`: ID of the NDLA article.
- `url`: URL to the article via the NDLA API.
- `section`: Section of the article (e.g., `article`, `intro`, or `meta`).
- `paragraph_index`: Index of the paragraph within the article.
- `nb`: Paragraph text in Norwegian Bokmål.
- `nn`: Corresponding translation in Norwegian Nynorsk.
- `nb_license`: License for the Bokmål paragraph.
- `nn_license`: License for the Nynorsk paragraph.
- `nb_creators`: Creator metadata for the Bokmål paragraph.
- `nn_creators`: Creator metadata for the Nynorsk paragraph.
- `article_paragraph_count`: Total number of paragraphs in the article.
---
### Data Splits
| Split | Size |
|-------------|----------|
| Train | 206,000 |
| Validation | 1,020 |
| Test | 1,040 |
---
## Usage
You can load the dataset using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("NbAiLab/ndla_parallel_paragraphs")
# Access the training split
train_data = dataset["train"]
# Example: print the first entry
print(train_data[0])
```
Example output:
```json
{
"id": "nbnn_article_7_0",
"article_id": 7,
"url": "https://api.ndla.no/article-api/v2/articles/7",
"section": "article",
"paragraph_index": 0,
"nb": "Kvantitativ er et adjektiv som er avledet av substantivet kvantitet...",
"nn": "Kvantitativ er eit adjektiv som er avleidd av substantivet kvantitet...",
"nb_license": "CC-BY-SA-4.0",
"nn_license": "CC-BY-SA-4.0",
"nb_creators": [{"type": "writer", "name": "Clemens Saers"}],
"nn_creators": [{"type": "writer", "name": "Clemens Saers"}],
"article_paragraph_count": 14
}
```
---
## Intended Use
This dataset is suitable for:
- Training and evaluating machine translation models between Norwegian Bokmål and Nynorsk.
- Linguistic and educational content analysis.
- Language modeling and other NLP tasks involving Norwegian.
---
## Limitations
- Paragraph alignment is based on structural position, not semantic matching — minor mismatches may occur.
- Dataset content is educational in nature and may not generalize to informal or non-academic language use.
- Some paragraphs may be skipped if empty or malformed in the source data.
---
## License
This dataset is distributed under the **Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)** license, as per the licensing terms of [NDLA.no](https://ndla.no).
---
## Acknowledgments
We extend our thanks to the [Norwegian Digital Learning Arena (NDLA)](https://ndla.no) for making the article content publicly available via their API.
This dataset has been curated and adapted for Hugging Face by **Andre Kåsen** and **Per Egil Kummervold**. |