Sentence Similarity
sentence-transformers
Safetensors
multilingual
bert
feature-extraction
Generated from Trainer
dataset_size:2320
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ValentinaKim/Multilingual-base-soil-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ValentinaKim/Multilingual-base-soil-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ValentinaKim/Multilingual-base-soil-embedding") sentences = [ "MVGO; medium vacuum\ngas oil", "과분해", "Medium Vacuum Gas Oil(MVGO) ;", "선적 전 또는 양하 후에 화물창에 잔존하는 소량의 액체화물 양을 결정하는 수학\n적인 계산 수식" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6803d80dfd27b26211a967d9e4a230d7caffa8564ff4adb8f96610825c9b6c92
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
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