Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1473
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use George2002/duplicates_checker_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use George2002/duplicates_checker_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("George2002/duplicates_checker_v1") sentences = [ "А если я ИП или самозанятый, получу ли я софинансирование по ПДС?", "Возможны программы 'Ипотека без ПВ под залог имеющегося жилья' или 'Нецелевой кредит под залог'.", "В чем разница между именной и моментальной кредитной картой?", "Да, ИП и самозанятые могут получать софинансирование по ПДС." ] 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" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |