Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
csv
Languages:
Arabic
Size:
100K - 1M
Tags:
sentence-transformers
License:
Transferred from Omartificial-Intelligence-Space/Arabic-NLi-Triplet
Browse files- .gitattributes +2 -4
- README.md +65 -0
- test.csv +0 -0
- train.csv +3 -0
- validation.csv +0 -0
.gitattributes
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train.csv filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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task_categories:
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- sentence-similarity
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language:
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- ar
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size_categories:
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- 10K<n<100K
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tags:
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- sentence-transformers
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---
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# Arabic NLI Triplet
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## Dataset Summary
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1. The Arabic Version of SNLI and MultiNLI datasets. (Triplet Subset)
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2. Originally used for Natural Language Inference (NLI),
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3. Dataset may be used for training/finetuning an embedding model for semantic textual similarity.
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## Triplet Subset
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- Columns: "anchor", "positive", "negative"
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- Column types: str, str, str
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Examples:
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```python
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{
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"anchor": "شخص على حصان يقفز فوق طائرة معطلة",
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"positive": "شخص في الهواء الطلق، على حصان.",
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"negative": "شخص في مطعم، يطلب عجة."
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}
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```
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## Disclaimer
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Please note that the translated sentences are generated using neural machine translation and may not always convey the intended meaning accurately.
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## Contact
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[Contact Me](https://www.omarai.co) if you have any questions or you want to use thid dataset
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## Note
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Original work done by [SentenceTransformers](https://www.sbert.net)
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## Citation
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If you use the Arabic Matryoshka Embeddings Dataset, please cite it as follows:
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```bibtex
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@misc{nacar2024enhancingsemanticsimilarityunderstanding,
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title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning},
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author={Omer Nacar and Anis Koubaa},
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year={2024},
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eprint={2407.21139},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.21139},
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}
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test.csv
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train.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c607c4f7000f1cad3692593eb349a64f1b7f89dffac6a972adb97189dd0fe82
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size 132437986
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validation.csv
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