Sentence Similarity
sentence-transformers
Safetensors
Arabic
bert
mteb
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Omartificial-Intelligence-Space/Arabic-labse-Matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Omartificial-Intelligence-Space/Arabic-labse-Matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-labse-Matryoshka") sentences = [ "ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة", "رجل يقدم عرضاً", "هناك رجل بالخارج قرب الشاطئ", "رجل يجلس على أريكه" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:
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}
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If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:
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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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