Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:649352
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use uc-ctds/bge-large-en-v1.5-bio-mapping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use uc-ctds/bge-large-en-v1.5-bio-mapping with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("uc-ctds/bge-large-en-v1.5-bio-mapping") sentences = [ "dataCollection.studyDesignSummary: Plan detailing how data will be collected from patient cohorts.", "dataset.primary_site: The primary body site studied in this dataset.", "dataset.research_design: A summary of the goals of the research or a general description of the research's relationship to a clinical application.", "subject.taxon_id: NCBI identifier for classification of organisms." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a73e52b66c0cc77b8ce002b25f4b56af8bf2eb72e99c9aa2152a317e68c1b5c3
- Size of remote file:
- 1.34 GB
- SHA256:
- af9acf7efda2a5d954c826ad3eee9b915fc4f24ea3bfe706951e2bb061ca1801
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