The CTX recipe with Stage 1 (domain-adaptive MLM) skipped — contrastive straight off ModernBERT-base. Shows DAPT adds only ~0.1 once Signals A+B are present.
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-nodapt-ctx")
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
28.2
80.8
82.6
66.7 ± 0.07
Citation
See the repository README. Paper:
SciEmbed: Citation-Context Supervision for Scientific Document Embeddings
(under review).
Runs of anon-nlp sciembed-nodapt-ctx on huggingface.co
94
Total runs
0
24-hour runs
1
3-day runs
3
7-day runs
6
30-day runs
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