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_v03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CocoRoF/ModernBERT-SimCSE_v03 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CocoRoF/ModernBERT-SimCSE_v03") 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
Upload config_sentence_transformers.json with huggingface_hub
Browse files
config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "3.3.1",
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"transformers": "4.48.0",
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"pytorch": "2.5.1+cu124"
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},
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"prompts": {},
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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