Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:649257
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine") sentences = [ "elephant ear alocasia", "peace", " plant", "plant" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 6
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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3.9416633819471816,10000,0.9752865433692932
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4.3358297201419,11000,0.9754968881607056
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3.9416633819471816,10000,0.9752865433692932
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4.729996058336618,12000,0.9761278629302979
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model.safetensors
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