Sentence Similarity
sentence-transformers
Safetensors
Japanese
llama
feature-extraction
dense
Generated from Trainer
dataset_size:12451
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use kushalc1/sarashina-embedding-v2-1b-jsts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kushalc1/sarashina-embedding-v2-1b-jsts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kushalc1/sarashina-embedding-v2-1b-jsts") sentences = [ "草原で2頭のシマウマが草を食べています。", "芝の上に5体象のオブジェが置いてあります。", "テーブルトップが大理石になってる台所です。", "草地にシマウマが二頭並んで草を食べています。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 729f4b16b24503bb73c26083537538159ba3b17dbe0d2abace9b7e15b166d281
- Size of remote file:
- 1.83 MB
- SHA256:
- 008293028e1a9d9a1038d9b63d989a2319797dfeaa03f171093a57b33a3a8277
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