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/MiniLM-V18Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V18Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-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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1.0877447425670776,3000,0.9545693397521973
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1.4503263234227701,4000,0.9578294157981873
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1.8129079042784626,5000,0.9597223401069641
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1.0877447425670776,3000,0.9545693397521973
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1.4503263234227701,4000,0.9578294157981873
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1.8129079042784626,5000,0.9597223401069641
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2.1754894851341553,6000,0.9615101218223572
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2.5380710659898478,7000,0.9624566435813904
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2.9006526468455403,8000,0.9623514413833618
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 90864192
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