Sentence Similarity
sentence-transformers
Safetensors
Hebrew
hebrew
semantic-retrieval
information-retrieval
dense-retrieval
reranking
ensemble
competition
Instructions to use HebArabNlpProject/Semantic-Retrieval-1st-place with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HebArabNlpProject/Semantic-Retrieval-1st-place with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HebArabNlpProject/Semantic-Retrieval-1st-place") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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## Acknowledgements
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- [Webiks](https://www.webiks.com/) for the `Hebrew-RAGbot-KolZchut-QA-Embedder-v1.0` model.
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- The authors of `multilingual-e5-large`, `bge-m3`, `bge-reranker-v2-m3`, `snowflake-arctic-embed-l-v2.0`, and `Solon-embeddings-large-0.1`.
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## Acknowledgements
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- MAFAT DDR&D and the **Israel National NLP Program** for organizing the challenge and providing the annotated Hebrew corpus.
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- [Webiks](https://www.webiks.com/) for the `Hebrew-RAGbot-KolZchut-QA-Embedder-v1.0` model.
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- The authors of `multilingual-e5-large`, `bge-m3`, `bge-reranker-v2-m3`, `snowflake-arctic-embed-l-v2.0`, and `Solon-embeddings-large-0.1`.
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