Transferred from Omartificial-Intelligence-Space/Arabic-stsb

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README.md

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+---
+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}, 
+}