Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:8690
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use vany02/hitachi-defect-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vany02/hitachi-defect-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vany02/hitachi-defect-classifier") sentences = [ "1位側開戸排水パン内 切粉(最大3mm×5ヶ)", "切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。", "ひっかき傷に類するキズがあるもの", "塗装の色むら・色違い等。ただし、塗装自体が未了の場合は「503作業漏れ」に分類する。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle