Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:355097
loss:CategoricalContrastiveLoss
Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_3 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_3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_3") sentences = [ "科目:コンクリート。名称:EXP_J充填コンクリート。", "科目:コンクリート。名称:普通コンクリート。", "科目:タイル。名称:外壁ガラスモザイクタイル張り。", "科目:タイル。名称:段鼻タイル。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca237939c95781287da41007a91ab8716c4f5b22b18b8d621ed34f0cde01a764
- Size of remote file:
- 445 MB
- SHA256:
- 4cd90f0a4c10d69741b2a85fcd3c127b517dbdf1dc6183ac77aa2001c913b71e
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