Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:1765391
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use smartcat/SRBedding-base-distilled-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use smartcat/SRBedding-base-distilled-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smartcat/SRBedding-base-distilled-v1") sentences = [ "The virus of the October Revolution spread over the Kingdom too.", "Webinar: Rad od kuće - rizici i izazovi za poslodavce u Srbiji (Video) - TSG", "Zbog toga i uprkos savetima prijatelja koji mu predviđaju neuspeh, odluči da da otputuje u mesto svoje izabranice, u srce zemlje Baska, kako bi je ubedio da je ona ljubav njegovog života i da je on pravi čovek za nju.", "Virus Oktobarske revolucije zahvatio je i Kraljevinu." ] 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" | |
| } | |
| ] |