Feature Extraction
sentence-transformers
Safetensors
English
modernbert
sentence-similarity
scientific-documents
citation-context
text-embeddings-inference
Instructions to use anon-nlp/sciembed-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anon-nlp/sciembed-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anon-nlp/sciembed-base") 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
- Xet hash:
- 7a56de68badf9c1a3436cfb8ec73e2f17d1d448daf73b8e7ec0f006cad863a1b
- Size of remote file:
- 596 MB
- SHA256:
- 6e146c6f40261eadde5805dae5d8ce68723d2becd1502d9c9765353b8b9a16a7
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