Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:4984
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
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| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
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| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
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