Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1148773
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine") sentences = [ "rosa / porcelain us andalusia mug", "klara and the sun", " mug", "mug" ] 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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2.450980392156863,11000,0.9767588376998901
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2.6737967914438503,12000,0.9763382077217102
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2.8966131907308377,13000,0.9762330651283264
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2.450980392156863,11000,0.9767588376998901
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2.8966131907308377,13000,0.9762330651283264
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3.1194295900178255,14000,0.9758123755455017
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3.5650623885918002,16000,0.9768640398979187
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model.safetensors
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size 90864192
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