Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:20000
loss:CosineSimilarityLoss
Instructions to use yazied49/NAdine3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yazied49/NAdine3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yazied49/NAdine3") sentences = [ "Question: Is this describing a (1) directly correlative relationship, (2) conditionally causative relationship, (3) causative relationship, or (0) no relationship.", "C: Iron deficiency anemia in the mother; normal Hb levels in the fetus", "This is a conditionally causative relationship", "C: Decreasing carbohydrate intake, increasing fat intake" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle