---
license: apache-2.0
task_categories:
- sentence-similarity
language:
- ar
tags:
- sentence-transformers
size_categories:
- 1K<n<10K
---


## Arabic STSB Structure

- The Arabic Version  of the the Semantic Textual Similarity Benchmark (Cer et al., 2017)
- it is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data.
- Each pair is human-annotated with a similarity score from 1 to 5. However, for this variant, the similarity scores are normalized to between 0 and 1.

Examples:

```python
{
  "sentence1": "طائرة ستقلع",
  "sentence2": "طائرة جوية ستقلع",
  "score": 1.0
}

{
  "sentence1": "رجل يعزف على ناي كبير",
  "sentence2": "رجل يعزف على الناي.",
  "score": 0.76
}
```

## Collection strategy:
- Reading the sentences and score from the STSB dataset and dividing the score by 5.
- Deduplified: No

## Disclaimer
Please note that:
- the translated sentences are generated using neural machine translation and may not always convey the intended meaning accurately.
- the similarity scores are normalized, and the original scores were between 1 and 5.

## Contact
[Contact Me](https://www.omarai.co) if you have any questions or you want to use thid dataset

## Note

Original work done by [SentenceTransformers](https://www.sbert.net)

## Citation

If you use the Arabic Matryoshka Embeddings Dataset, please cite it as follows:

```bibtex
@misc{nacar2024enhancingsemanticsimilarityunderstanding,
      title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning}, 
      author={Omer Nacar and Anis Koubaa},
      year={2024},
      eprint={2407.21139},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.21139}, 
}