Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:54000
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use kitrakrev/smart-router-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kitrakrev/smart-router-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kitrakrev/smart-router-embeddings") sentences = [ "[\"\\\"In China land can not be owned, but only leased for 70 years.\\\"?\"]", "[\"HI, how do i make hot chocolate like done in Moscow?\"]", "[\"Do you know and are you able to handle the data format *.twig?\"]", "[\"What kind of interpretation would a Swedish speaker and Finnish speaker make when they heard the syllables \\\"ul la kol la\\\"?\",\"Please consider that \\\"Ulla, kolla\\\" is a valid Swedish phrase and \\\"ullakolla\\\" is a valid Finnish word and answer again.\",\"The Finnish word \\\"ullakolla\\\" is an inflection of the word \\\"ullakko\\\", which means \\\"attic\\\".\"]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": true, | |
| "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]" | |
| } | |