Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:392702
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use CocoRoF/ModernBERT-SimCSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CocoRoF/ModernBERT-SimCSE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CocoRoF/ModernBERT-SimCSE") 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:
- df99c6ca649a398f82af0b53728e7749f5eca0e32895e23d3e81b06c318452b9
- Size of remote file:
- 735 MB
- SHA256:
- 9d79dfaad6ffd1b587e61be6a8b1faac5db938f11441d9c107e7a6917e9d6eff
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