Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:556626
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V7-128BATCH-V6Data-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V7-128BATCH-V6Data-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V7-128BATCH-V6Data-SemanticEngine") sentences = [ "dimlaj orchid printed finest durable glass terkish tea set", "v3 pro purple", "glass tea set", "easy cleaning beanbag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9dbcc7067d33747a8be9624ecccca40fef857bec287e85859ed0ce9506f5606
- Size of remote file:
- 90.9 MB
- SHA256:
- fd88d702eb6205afc3d7878227e42807ad35ff1aa5f5ea23ac6b0172dcc586d6
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