Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
modernbert
feature-extraction
text-retrieval
scientific-papers
literature-search
arxiv
Eval Results (legacy)
text-embeddings-inference
Instructions to use daniel-gomm/scientific-granite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use daniel-gomm/scientific-granite with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("daniel-gomm/scientific-granite") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!