---
dataset_info:
  features:
  - name: question
    dtype: string
  - name: context
    dtype: string
  - name: score
    dtype: float64
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: answers
    struct:
    - name: answer_start
      sequence: int64
    - name: text
      sequence: string
  splits:
  - name: train
    num_bytes: 127996360
    num_examples: 130319
  - name: dev
    num_bytes: 10772220
    num_examples: 10174
  - name: test
    num_bytes: 1792665
    num_examples: 1699
  download_size: 18702176
  dataset_size: 140561245
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
license: cc-by-sa-4.0
language:
- nl
task_categories:
- sentence-similarity
- question-answering
tags:
- sentence-transformers
pretty_name: SQuAD-NL v2.0 for Sentence TGransformers
---
# SQuAD-NL v2.0 for Sentence Transformers

The [SQuAD-NL v2.0](https://github.com/wietsedv/NLP-NL/tree/squad-nl-v1.0?tab=readme-ov-file#-squad-nl-translated-squad--xquad-question-answering) dataset (on Hugging Face: [GroNLP/squad-nl-v2.0](https://huggingface.co/datasets/GroNLP/squad-nl-v2.0)), modified for use in [Sentence Transformers](https://sbert.net/docs/sentence_transformer/dataset_overview.html) as a dataset of type "Pair with Similarity Score".

## Score
We added an extra column `score` to the original dataset.
The value of `score` is `1.0` if the question has an answer in the context (no matter where), and `0.0` if there are no answers in the context.
The allows the evaluation of embedding models that aim to pair queries and document fragments.
Please note that is a very hard task for embedding models, because SQuAD v2.0 was specifically designed to contain unanswerable questions adversarially written to look similar to answerable ones.
Expect your models to perform poorly.

## Translations
SQuAD-NL is translated from the original [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) and [XQuAD](https://github.com/google-deepmind/xquad) English-language datasets.
From the [SQuAD-NL v2.0 Readme](https://github.com/wietsedv/NLP-NL/tree/squad-nl-v1.0?tab=readme-ov-file#v20):

| Split | Source                 | Procedure                | English | Dutch   |
| ----- | ---------------------- | ------------------------ | ------: | ------: |
| train | SQuAD-train-v2.0       | Google Translate         | 130,319 | 130,319 |
| dev   | SQuAD-dev-v2.0 \ XQuAD | Google Translate         |  10,174 |  10,174 |
| test  | SQuAD-dev-v2.0 & XQuAD | Google Translate + Human |   1,699 |   1,699 |

For testing Dutch sentence embedding models it is therefore recommended to only use the `test` split.
Also it would be advisable to not train your model on the other splits, because you would train answering this specific style of questions into the model.

## Example code using Sentence Transformers
```python
import pprint

from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator, SimilarityFunction


eval_dataset = load_dataset('NetherlandsForensicInstitute/squad-nl-v2.0', split='test')

evaluator = EmbeddingSimilarityEvaluator(
     sentences1=eval_dataset['question'],
     sentences2=eval_dataset['context'],
     scores=eval_dataset['score'],
     main_similarity=SimilarityFunction.COSINE,
     name="squad_nl_v2.0_test",
 )

model = SentenceTransformer('NetherlandsForensicInstitute/robbert-2022-dutch-sentence-transformers')

results = evaluator(model)
pprint.pprint(results)
```

## Original dataset
SQuAD-NL is a derivative of the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) and [XQuAD](https://github.com/google-deepmind/xquad) datasets, and their original [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/legalcode) licenses apply.

## Code used to generate this dataset
<details>
<summary>code</summary>
  
```python
import json

import requests
from datasets import Dataset, DatasetDict


def squad(url):
  response = requests.get(url)

  rows = json.loads(response.text)['data']

  for row in rows:
      yield {'question': row['question'],
             'context': row['context'],
             'score': 1.0 if row['answers']['text'] else 0.,
             'id': row['id'],
             'title': row['title'],
             'answers': row['answers']}

if __name__ == '__main__':
  url = 'https://github.com/wietsedv/NLP-NL/raw/refs/tags/squad-nl-v1.0/SQuAD-NL/nl/{split}-v2.0.json'

  dataset = DatasetDict({
      split: Dataset.from_generator(squad, gen_kwargs={'url': url.format(split=split)})
      for split in ('train', 'dev', 'test')
  })

  dataset.push_to_hub('NetherlandsForensicInstitute/squad-nl-v2.0')
```

</details>