Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3277
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ds7619tz/fine-tuned-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ds7619tz/fine-tuned-stsb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ds7619tz/fine-tuned-stsb") sentences = [ "myanmar is the world's largest producer of illegal opium but has limited financial and technical resources for ending the drug trade.", "A man is playing the trumpet.", "myanmar is the world's largest producer of illicit opium according to the latest u.s. state department anti-narcotics report.", "The theme song to Cheers: We are all vessels filled with many wonders." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K