Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:705905
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine") sentences = [ "gerber baby food fruits apples bananas & cereal", "world of sweets puzzle", "baby food", "baby food" ] 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 +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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1.0876811594202898,3000,0.9617204666137695
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1.45,4000,0.9664528369903564
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1.8123188405797102,5000,0.9669786691665649
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1.0876811594202898,3000,0.9617204666137695
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1.45,4000,0.9664528369903564
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1.8123188405797102,5000,0.9669786691665649
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2.17463768115942,6000,0.9669786691665649
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2.5369565217391306,7000,0.9693974256515503
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2.8992753623188405,8000,0.9703438878059387
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model.safetensors
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size 90864192
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