Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:605748
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V17Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V17Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V17Data-128BATCH-SemanticEngine") sentences = [ "sand eel shad soft lure combo eelo 150 25 g ayu/blue", "marvel na! na! na! surprise 2-pack air arms multicolor", "fast fishing fishing lure", "fishing" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f45fe4c9c4b3d74d6cbd157a22331ed0122ed715caedf040e709bbfd85c11eff
- Size of remote file:
- 90.9 MB
- SHA256:
- 5884560ff57238fe77c5b0de9e0b9fef0a8bb62c939b46cd0a31e268e97098b4
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