Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:989791
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v31-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v31-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v31-SemanticEngine") sentences = [ "turmeric", "essential oils", "joint comfort essential oil", "bubble enigma" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files- eval/triplet_evaluation_results.csv +4 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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2.326699405531145,9000,0.9706348180770874
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2.585164125096924,10000,0.9734868407249451
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2.8436288446627036,11000,0.9726418256759644
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2.326699405531145,9000,0.9706348180770874
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2.8436288446627036,11000,0.9726418256759644
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3.6190230033600415,14000,0.9738037586212158
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3.877487722925821,15000,0.9725362062454224
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model.safetensors
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