Feature Extraction
sentence-transformers
Safetensors
English
modernbert
sentence-similarity
scientific-documents
citation-context
text-embeddings-inference
Instructions to use J0nasW/sciembed-ctx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use J0nasW/sciembed-ctx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("J0nasW/sciembed-ctx") 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] - Notebooks
- Google Colab
- Kaggle
File size: 283 Bytes
f46e78b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.2.3",
"transformers": "4.57.6",
"pytorch": "2.7.0+cu126"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |