Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:458830
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine") sentences = [ "derby cap toe shoes - brown", "chained strapped block heeled sandals", "100% premium natural leather - high quality sole.", "puppy treats biscuits" ] 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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2.2315202231520224,8000,0.9575139284133911
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2.510460251046025,9000,0.9599326848983765
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2.789400278940028,10000,0.9591965675354004
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2.2315202231520224,8000,0.9575139284133911
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2.510460251046025,9000,0.9599326848983765
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2.789400278940028,10000,0.9591965675354004
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3.0683403068340307,11000,0.961194634437561
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3.6262203626220364,13000,0.9618256092071533
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3.905160390516039,14000,0.9621411561965942
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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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