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Omartificial-Intelligence-Space/Arabic-NLi-Pair-Score

Arabic NLI Pair-Score Dataset Summary The Arabic Version of SNLI and MultiNLI datasets. (Pair-Score Subset) Originally used for Natural Language Inference (NLI), Dataset may be used for training/finetuning an embedding model for semantic textual similarity. Pair-Class Subset Columns: "sentence1", "sentence2", "score" Column types: str, str, float Arabic Examples: { "sentence1": "شخص على حصان يقفز فوق طائرة معطلة", "sentence2":… See the full description on the dataset page: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Pair-Score.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Dataset Card

Arabic NLI Pair-Score

Dataset Summary

—The Arabic Version of SNLI and MultiNLI datasets. (Pair-Score Subset)
—Originally used for Natural Language Inference (NLI),
—Dataset may be used for training/finetuning an embedding model for semantic textual similarity.

Pair-Class Subset

—Columns: "sentence1", "sentence2", "score"
—Column types: str, str, float

Arabic Examples:

python

{
  "sentence1": "شخص على حصان يقفز فوق طائرة معطلة",
  "sentence2": "شخص يقوم بتدريب حصانه للمنافسة",
  "score": 0.5,
},
{
  "sentence1": "شخص على حصان يقفز فوق طائرة معطلة",
  "sentence2": "شخص في مطعم، يطلب عجة.",
  "score": 0,
},
{
  "sentence1": "شخص على حصان يقفز فوق طائرة معطلة",
  "sentence2": "شخص في الهواء الطلق، على حصان.",
  "score": 1,
}

Disclaimer

Please note that the translated sentences are generated using neural machine translation and may not always convey the intended meaning accurately.

Contact

Contact Me if you have any questions or you want to use thid dataset

Note

Original work done by SentenceTransformers

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}, 
}