Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:713598
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "100 horse riding sleeveless gilet - black", " must backpack ", "bag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 283 Bytes
faa16e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"sentence_transformers": "5.1.2",
"transformers": "4.53.3",
"pytorch": "2.6.0+cu124"
},
"model_type": "SentenceTransformer",
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |