Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:40656
loss:CachedGISTEmbedLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Aditya109/us-openfda-drug-embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Aditya109/us-openfda-drug-embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Aditya109/us-openfda-drug-embed-v1") sentences = [ "Brand: cough drops dollar general menthol 80ct. Ingredients: menthol. Strength: 5.4 mg/1. Form: lozenge. Route: oral.", "Brand: dermfree numbing. Ingredients: menthol. Strength: 5 g/100g. Form: cream. Route: topical.", "Brand: equate menthol cough drops. Ingredients: menthol. Strength: 5.4 mg/1. Form: lozenge. Route: oral.", "Brand: medline. Ingredients: cetirizine hydrochloride. Strength: 10 mg/1. Form: tablet. Route: oral." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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