Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:366717
loss:CategoricalContrastiveLoss
Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1") sentences = [ "科目:コンクリート。名称:免震BPL下部充填コンクリート打設手間。", "科目:コンクリート。名称:#F/#FLコンクリート打設手間。", "科目:コンクリート。名称:擁壁部コンクリート打設手間。", "科目:タイル。名称:EXP_J上床磁器質タイルA。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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