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
File size: 221 Bytes
1b4347a | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.3.1",
"transformers": "4.48.0",
"pytorch": "2.5.1+cu124"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |