Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:713598
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V23Data-256hardnegativesBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V23Data-256hardnegativesBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V23Data-256hardnegativesBATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "100 horse riding sleeveless gilet - black", " must backpack ", "bag" ] 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 +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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0.3586800573888092,1000,0.9522557854652405
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0.7173601147776184,2000,0.9615101218223572
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epoch,steps,accuracy_cosine
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0.3586800573888092,1000,0.9522557854652405
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0.7173601147776184,2000,0.9615101218223572
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1.0760401721664274,3000,0.963718593120575
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1.4347202295552366,4000,0.9657166600227356
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1.793400286944046,5000,0.9667683243751526
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model.safetensors
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size 90864192
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