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
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |