Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:512
loss:TripletLoss
dataset_size:243060
text-embeddings-inference
Instructions to use sirabhop/mart-multilingual-semantic-search-miniLM-L12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sirabhop/mart-multilingual-semantic-search-miniLM-L12 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sirabhop/mart-multilingual-semantic-search-miniLM-L12") sentences = [ "ตะกร้าแมว", "ตะกร้าหูหิ้วมีฝาปิดล็อคได้ ตะกร้าแมวเล็ก 15x23 ซม.", "พูกันกลม ตราม้า No.10", "101259 - ปลั๊กแปลง 2 ขาแบน TOSHINO CO-6S ขาว" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c6ea87e3e52bb3f864db4c88be6cd2c0d66d17042d8cf403fb3b0642cf431f1
- Size of remote file:
- 14.8 MB
- SHA256:
- da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
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