Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:392702
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use CocoRoF/lo_SimCSE_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CocoRoF/lo_SimCSE_test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CocoRoF/lo_SimCSE_test") sentences = [ "우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo 쪽으로. \"", "두 마리의 독수리가 가지에 앉는다.", "다른 물체와는 관련이 없는 '정지'는 없다.", "소녀는 버스의 열린 문 앞에 서 있다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e7f3348d21cb562f47eea9e4000524e8cd8f3394826f1f8f5a6d7309a29d569
- Size of remote file:
- 596 MB
- SHA256:
- 90730521d1afeebd4a9071e99504374254f02b43d461a97c64604be210101b2b
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