Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:1602
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AkshaySandbox/pregnancy-mpnet-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AkshaySandbox/pregnancy-mpnet-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AkshaySandbox/pregnancy-mpnet-embeddings") sentences = [ "Has there been any recent discussion on the trend of women choosing to become mothers later in life?", "British Columbia, Ontario, New Brunswick, Nova Scotia, and Prince Edward Island have fully implemented universal hearing screening programs.", "If the first readings exceed the maximum allowable difference, measurements are taken for a second and, if necessary, a third time.", "In recent years, practices have shifted and these professionals are now able to observe, assess, and consult on the child’s program at the centre rather than in an office visit." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K