Sentence Similarity
sentence-transformers
Safetensors
Hebrew
hebrew
semantic-retrieval
information-retrieval
dense-retrieval
reranking
bge-m3
competition
Instructions to use HebArabNlpProject/Semantic-Retrieval-3rd-place with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HebArabNlpProject/Semantic-Retrieval-3rd-place with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HebArabNlpProject/Semantic-Retrieval-3rd-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
- Xet hash:
- 3d3a9ab194466b6e3502faf1baa7aa66ed010b9dc04a580da3c830849b64c149
- Size of remote file:
- 17.1 MB
- SHA256:
- 33cd99e33ce09bdd8a6136fddfe90a1c47f85bafedf7309d0eecc19012d43586
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.