Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:356381
loss:CategoricalContrastiveLoss
Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_5 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_8_5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_5") sentences = [ "科目:コンクリート。名称:基礎コンクリート。", "科目:コンクリート。名称:免震上部コンクリート。", "科目:コンクリート。名称:立上り壁コンクリート。", "科目:コンクリート。名称:地上部コンクリート。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93a36f4b59333ec25261f9cbd87157e4de86219bc4553a395727e339ef1dbc0d
- Size of remote file:
- 445 MB
- SHA256:
- cb6701e400acf4a6f64381d341efff6b48ddf185b2f06a319d5c374333d4e289
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