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sciembed-nodapt-ctx
J0nasW/sciembed-nodapt-ctx
sciembed-nodapt-ctx is a feature extraction model from J0nasW. Use it when you need embeddings to search or compare text. It is set up for sentence-transformers. The card lists the license as mit.
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.
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At a glance
- Task
- Feature Extraction
- Library
- sentence-transformers
- License
- mit
- Model type
- modernbert
- Access
- Public
- Created
- Aug 27, 2026
- Updated
- Aug 27, 2026
- SHA
- 45fcdfbe
Base models
Model card
From the Hugging Face model README
SciEmbed-NoDAPT-CTX
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 (Findings of EMNLP 2026).
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("J0nasW/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, Findings of the Association for Computational Linguistics: EMNLP 2026.
- Task
- Feature Extraction
- Library
- sentence-transformers
- Type
- modernbert
- License
- mit
- Languages
- en
- Created
- Aug 27, 2026
- Updated
- Aug 27, 2026