Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1006385
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "tarte 4 in 1 mini mascara", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06eb81078a5dce8f9f8a4bb29d51187298a81ba063bad2e479f67872c94888ed
- Size of remote file:
- 90.9 MB
- SHA256:
- d9f6927a686b7ae554c8da1d0ac6ad150f2d253693e0f35e2d4338c3bf2ac987
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.