Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:574389
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use josangho99/ko-multilingual-e5-small-multiTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use josangho99/ko-multilingual-e5-small-multiTask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("josangho99/ko-multilingual-e5-small-multiTask") sentences = [ "전 나치 죽음의 수용소 경비원 뎀잔주크 91세 사망", "나치 사형수 수용소 경비원으로 유죄 판결을 받은 존 뎀잔죽 은 91세의 나이로 사망한다", "2040년까지 은퇴자들은 인구의 3분의 1을 차지할 것이며, 이는 오늘의 5분의 1에서 증가할 것이다.", "이집트에서 살해된 스카이 뉴스 카메라맨" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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