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
Download models/test_encoder_only_base_bge_m3_new1/training_args.bin from HebArabNlpProject/Semantic-Retrieval-3rd-place: direct link, hf CLI and curl.
- Browser
- Download file 7.95 kB
-
https://huggingface.co/HebArabNlpProject/Semantic-Retrieval-3rd-place/resolve/main/models/test_encoder_only_base_bge_m3_new1/training_args.bin
- Command line
-
hf download hf://HebArabNlpProject/Semantic-Retrieval-3rd-place/models/test_encoder_only_base_bge_m3_new1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HebArabNlpProject/Semantic-Retrieval-3rd-place/resolve/main/models/test_encoder_only_base_bge_m3_new1/training_args.bin
7.95 kB
- Xet hash:
- 7e02594a741ea9dd34573ccb1bcfa9b5d5adb9767c79fc0e11f4b0c650873649
- Size of remote file:
- 7.95 kB
- SHA256:
- 98887f48be1b4da068e277e1b7af2ed224e2109f28f6709c40d8c2202ffe2c24
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.