Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:164
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use ntucool/mlogging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ntucool/mlogging with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ntucool/mlogging") sentences = [ "qa_234", "1客戶主軸馬達編碼器異常主軸馬達送修拿備品安裝聯軸器廠商安裝並校正動平衡我司協助裝回", "故障狀況 追加皮帶式油水分離機 處理狀況 備料為客戶追加", "追加皮帶式油水分離機" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66f165c48a8b648c45dcb23b6360c64a0ca3d7e362c37755ec12196e78e3afd8
- Size of remote file:
- 95.8 MB
- SHA256:
- 13aab83f84858b4937acf601d695ed73c8c17ded40c5b689c5d9043270e52c77
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