Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use shubham-t/plagiarism-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shubham-t/plagiarism-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shubham-t/plagiarism-sbert") sentences = [ "Army jets kill 38 militants in NW Pakistan air raids", "U.S. drone kills 4 militants in Pakistan", "mexico wishes to avoid more violence.", "- wolf I think stories like this are stupid." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K