SciEmbed-BASE
Signal A only (citation edges), 7M pairs, 3 epochs. The citation-edge baseline that isolates what Signal B adds.
A 149M-parameter ModernBERT-base scientific document embedder trained with citation-context sentences as the primary contrastive signal. Part of the SciEmbed release (paper under double-blind review; author info omitted).
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("anon-nlp/sciembed-base")
emb = model.encode(["citation-context supervision for scientific embeddings"],
normalize_embeddings=True)
- Context length: 512 tokens
- Pooling: mean · Output dim: 768 (Matryoshka-truncatable to 512/256/128)
- License: MIT
SciRepEval (4-category macro)
| Classif. | Regr. | Prox. | Search | Overall |
|---|---|---|---|---|
| 75.3 | 26.8 | 80.2 | 82.2 | 66.1 ± 0.09 |
Citation
See the repository README. Paper: SciEmbed: Citation-Context Supervision for Scientific Document Embeddings (under review).
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Model tree for anon-nlp/sciembed-base
Base model
answerdotai/ModernBERT-base
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]