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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |