Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:554030
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine") sentences = [ "pacman smoked turkey", "omelette with fresh basil & cherry tomatoes", "mozzarella pacman", " tote " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files- eval/triplet_evaluation_results.csv +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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2.771362586605081,6000,0.9596002101898193
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3.695150115473441,8000,0.9600210189819336
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2.771362586605081,6000,0.9596002101898193
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4.157043879907621,9000,0.9589689373970032
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model.safetensors
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