Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:798
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use philipk22/ind312-ft-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use philipk22/ind312-ft-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("philipk22/ind312-ft-v0") sentences = [ "What is the definition of a sponsor-investigator according to the provided context?", "§ 312.47 Meetings.\n(a) General. Meetings between a sponsor and the agency are frequently useful in resolving questions and\nissues raised during the course of a clinical investigation. FDA encourages such meetings to the extent\nthat they aid in the evaluation of the drug and in the solution of scientific problems concerning the drug, to\nthe extent that FDA's resources permit. The general principle underlying the conduct of such meetings is", "employees to conduct an investigation that it has initiated is a sponsor, not a sponsor-investigator, and\nthe employees are investigators.\nSponsor-Investigator means an individual who both initiates and conducts an investigation, and under whose\nimmediate direction the investigational drug is administered or dispensed. The term does not include any\nperson other than an individual. The requirements applicable to a sponsor-investigator under this part", "practice regulations in part 58, or, if the study was not conducted in compliance with those\nregulations, a brief statement of the reason for the noncompliance.\n(9) Previous human experience with the investigational drug. A summary of previous human experience\nknown to the applicant, if any, with the investigational drug. The information is required to include\nthe following:\n(i) If the investigational drug has been investigated or marketed previously, either in the United" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K