Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:2976
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Borsa356/bert_mnr_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Borsa356/bert_mnr_3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Borsa356/bert_mnr_3") sentences = [ "Two girls in red soccer uniforms running after a soccer ball.", "A man is holding a girls hand and walking through a creek.", "A boy is in a competition.", "Two girls are playing a game of soccer." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K