README.md

---
license: cc-by-nc-3.0
task_categories:
- text-generation
language:
- it
- en
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: sciq.train.jsonl
  - split: validation
    path: sciq.validation.jsonl
  - split: test
    path: sciq.test.jsonl
---

# SciQ - Italian (IT)
This dataset is an Italian translation of [SciQ](https://arxiv.org/abs/1707.06209). SciQ is a dataset for scientific questions, which were semi-automatically generated from an existing set of questions. The dataset is designed to test the ability of models to answer questions that require scientific knowledge.

## Dataset Details
The dataset consists of science-related questions, where each question is associated with a correct answer and three possible distractors. The task is to predict the correct answer to the question. The dataset also provides a support passage for most questions, which can be used to answer the question. 

The dataset includes the following splits:
* Train: 11,615 rows
* Validation: 994 rows
* Test: 995 rows

### Differences with the original dataset
* The number of instances in this dataset is smaller than the original dataset due to the translation process, during which some instances were filtered out.

### Languages
This dataset is **fully parallel** between English and Italian. This allows us to have comparable evaluation setups and results across the two languages.

### Translation Process
The translation has been carried out using [🍱 OBenTO](), an open-source tool for LLM-based translation.
The main motivation for using an open-source LLM is to encourage free, open, reproducible, and transparent research in LLM evaluation.
See [🍱 OBenTO]() for more details on the translation process.

### Other Information
- **Original dataset by:** [Welbl et al.](https://arxiv.org/abs/1707.06209)
- **Translation by:** [Simone Conia](https://scholar.google.com/citations?user=S1tqbTcAAAAJ)
- **Languages:** Italian, English
- **License:** CC BY-NC 3.0

## Dataset Format
This is an example that shows the format of the dataset, where:
* `id`: a unique ID for each sample;
* `input`: the original English sentence in the dataset;
* `input_translation`: the translation of the sentence in Italian;
* `choices`: the original English choices;
* `choices_translation`: the translation of the choices in Italian;
* `label`: the index of the correct answer.
* `metadata`: additional information about the question, including the passage that should help with providing the answer.

#### Example of a question in SciQ
```json
{
  "id": "validation-00002",
  "input": "A frameshift mutation is a deletion or insertion of one or more of what that changes the reading frame of the base sequence?",
  "input_translation": "Una mutazione di spostamento del fotogramma è una delezione o un'inserzione di uno o più di cosa che cambia il fotogramma di lettura della sequenza di basi?",
  "label": 0,
  "choices": [
    "Nucleotides.",
    "Proteins.",
    "Carotenoids.",
    "Genes."
  ],
  "choices_translation": [
    "Nucleotidi.",
    "Proteine.",
    "Carotenoidi.",
    "Genomi."
  ],
  "metadata": {
    "category": "question",
    "passage": "A frameshift mutation is a deletion or insertion of one or more nucleotides that changes the reading frame of the base sequence. Deletions remove nucleotides, and insertions add nucleotides. Consider the following sequence of bases in RNA:.",
    "passage_translation": "Una mutazione di spostamento del fotogramma è una delezione o un'inserzione di uno o più nucleotidi che cambia il fotogramma di lettura della sequenza di basi. Le delezioni rimuovono nucleotidi e le inserzioni aggiungono nucleotidi. Considera la seguente sequenza di basi nell'RNA:."
  }
}
```

## License
The dataset is distributed under the CC BY-NC 3.0 license.

## Acknowledgements
I would like to thank the authors of the original dataset for making it available to the research community.
I would also like to thank [Future AI Research](https://future-ai-research.it/) for supporting this work and funding my research.

### Special Thanks
My special thanks go to:
* Pere-Lluís Huguet Cabot and Riccardo Orlando for their help with [🍱 OBenTO]().

## Dataset Card Authors
* [Simone Conia](https://scholar.google.com/citations?user=S1tqbTcAAAAJ): simone.conia@uniroma1.it