Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:604740
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine") sentences = [ "casa chandelier", "new eleganza - 6-999-x", "casa chandelier", "chandlier" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files- eval/triplet_evaluation_results.csv +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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0.4231908590774439,1000,0.9628772735595703
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0.8463817181548878,2000,0.9654011726379395
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epoch,steps,accuracy_cosine
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0.4231908590774439,1000,0.9628772735595703
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0.8463817181548878,2000,0.9654011726379395
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1.2693446088794926,3000,0.9682406187057495
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1.6921775898520086,4000,0.9717110395431519
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model.safetensors
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size 90864192
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