Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:12800
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Borsa356/bert_mnr_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Borsa356/bert_mnr_7 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Borsa356/bert_mnr_7") sentences = [ "Two boys are playing in pool filled with sparkling blue water.", "A boy plays in the pool.", "Woman holds her purse.", "The child is very brave to hold onto that tail." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K