Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:449904
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use CocoRoF/ModernBERT-SimCSE-multitask_v05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CocoRoF/ModernBERT-SimCSE-multitask_v05 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CocoRoF/ModernBERT-SimCSE-multitask_v05") 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
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |