Feature Extraction
sentence-transformers
Safetensors
Japanese
English
modernbert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use retrieva-jp/amber-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use retrieva-jp/amber-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("retrieva-jp/amber-base") 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:
- 729f4b16b24503bb73c26083537538159ba3b17dbe0d2abace9b7e15b166d281
- Size of remote file:
- 1.83 MB
- SHA256:
- 008293028e1a9d9a1038d9b63d989a2319797dfeaa03f171093a57b33a3a8277
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