Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1006385
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "tarte 4 in 1 mini mascara", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4
Browse files- eval/triplet_evaluation_results.csv +4 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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2.288256227758007,9000,0.9672941565513611
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2.5424504321301473,10000,0.968766450881958
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2.796644636502288,11000,0.9703438878059387
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2.288256227758007,9000,0.9672941565513611
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2.5424504321301473,10000,0.968766450881958
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2.796644636502288,11000,0.9703438878059387
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3.5592272496187087,14000,0.9692922234535217
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3.813421453990849,15000,0.9695025682449341
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model.safetensors
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size 90864192
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