Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:600313
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use dev7halo/Ko-sroberta-base-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dev7halo/Ko-sroberta-base-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dev7halo/Ko-sroberta-base-multitask") sentences = [ "사람은 무언가를 창조했다.", "한 남자가 악한 시기의 소동을 재현한다.", "한 사람이 고속도로에서 오토바이를 타고 있다", "개 두 마리가 있다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da20630d16d19bceefbea4c2faf667b70a6d530c7a03229751629f3f6bfd698d
- Size of remote file:
- 442 MB
- SHA256:
- b8d561088b96480addd8bd500b477fcc17925d554e64239a9396abcb30f92d67
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