Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:550152
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use x2bee/KoModernBERT_SBERT_compare_mlmlv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use x2bee/KoModernBERT_SBERT_compare_mlmlv5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("x2bee/KoModernBERT_SBERT_compare_mlmlv5") sentences = [ "한 남자가 다리가 허벅지에 있고 자전거 헬멧이 두 개 뒤에 있는 여자 옆에 앉아 있다.", "그 어린 소년은 야외에서 장난감 비행기를 날리고 있었다.", "사람들은 더 잘 보기 위해 서 있다.", "남자가 여자의 허벅지에 다리를 얹고 있다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
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