Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dataset_size:1K<n<10K
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ve88ifz2/snowflake-arctic-embed-m-klej-dyk-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ve88ifz2/snowflake-arctic-embed-m-klej-dyk-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ve88ifz2/snowflake-arctic-embed-m-klej-dyk-v0.1") sentences = [ "kim był Steve Yzerman?", "Łazik marsjański Opportunity", "w jakim kraju jest przyznawany Order Białego Lotosu?", "do powstania jakich instytucji przyczynił się pierwszy biskup Makau?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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