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aisuko
/
training

Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:4984
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use aisuko/training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use aisuko/training with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("aisuko/training")
    
    sentences = [
        "<think>\nLet’s think through this step by step\nrp = 500/month\nfp = 10/day\nlp = 60/lesson\nlpw = 2 lessons/week\nyp = 12 months/year\ntotal = (500 × 12) + (10 × 365) + (60 × 2 × 52)\ntotal = 6000 + 3650 + 6240\ntotal = 15890\n</think>\n\\boxed{15890}",
        "<think>\nLet’s think through this step by step\nrp = 500/month\nfp = 10/day\nlp = 60/lesson\ntp = (500 × 12) + (10 × 365) + (60 × 2 × 52)\ntp = 6000 + 3650 + 6240\ntp = 15890\n</think>\n\\boxed{15890}",
        "<think>\nLet’s think through this step by step\nh1 = 500 ft\nh2 = 2 * h1 = 2 * 500 = 1000 ft\nTotal height = h1 + h2 = 500 + 1000 = 1500 ft\n</think>\n\\boxed{1500}",
        "<think>\nLet’s think through this step by step\nfc = 200\nlc = 500\ndc = 500 × 0.8 = 400\nnc = 200 - 50 = 150\ntc = 150 + 400 = 550\n</think>\n\\boxed{550}"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
training / runs
43.1 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
aisuko's picture
aisuko
aisuko/encoder-L6-V2
573158c verified 10 months ago
  • Jul30_12-33-24_504bbaf3a637
    aisuko/encoder-L6-V2 10 months ago
  • Jul30_13-09-19_4e06a36bb887
    aisuko/encoder-L6-V2 10 months ago