Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:291522
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine") sentences = [ "cream 21 baby oil with almond oil", "hi, barbie! bundle", "nourishing baby oil", "material: wooden. size: 15 x 30 cm." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__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" | |
| } |