Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use shubham-t/fineTune-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shubham-t/fineTune-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shubham-t/fineTune-sbert") sentences = [ "Birthday of DPRK to Be Celebrated in Peru", "Curiosity Rover Celebrates 1 Year on Mars", "A man is playing a guitar.", "A guy is going up for a lay-up on a basketball court." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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