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
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "101": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "102": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "103": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "extra_special_tokens": {}, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 256, | |
| "never_split": null, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |