Instructions to use odunola/sentence-transformers-bible-reference-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use odunola/sentence-transformers-bible-reference-final with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("odunola/sentence-transformers-bible-reference-final") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use odunola/sentence-transformers-bible-reference-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="odunola/sentence-transformers-bible-reference-final")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("odunola/sentence-transformers-bible-reference-final") model = AutoModel.from_pretrained("odunola/sentence-transformers-bible-reference-final") - Notebooks
- Google Colab
- Kaggle
very cool, thanks!
Odunola, thanks for your work on these projects, it's super helpful.
You might this project I started recently interesting and it uses your model:
https://github.com/taymossimc/scriptureclassifier
It's a multi-stage pipeline designed to detect scripture whether these are citations ("John 3:16), verbatim quotes ("God so loved the world", NRSV, John 3:16), paraphases ("Our Father loved the world so much", Paraphrase, John 3:16) and so forth. The "sensitivity" and other settings can be adjusted. I know you were doing something similar for Easy Worship. We should definitely collaborate and share notes!