Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:30620
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use johnyy212/moe-girl-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use johnyy212/moe-girl-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("johnyy212/moe-girl-test") sentences = [ "长卷发、羽织、耳环、金瞳、王冠编发、面具", "群愿", "角色:傅园慧\n本名:傅园慧\n别名:傅爷、小园园、洪荒少女\n生日:1月7日\n星座:摩羯\n血型:O\n萌点:运动员、泳装、天然疯、天然萌、颜艺、表情包", "角色:群愿\n本名:群愿\n别名:叶永\n发色:棕\n瞳色:黄\n萌点:面具、浴衣、木屐、高额头、下双马尾、耳环、人字刘海、长卷发、蝴蝶结、王冠编发、高马尾、羽织、高跟鞋" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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