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license: mit
library_name: sentence-transformers
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- scientific-documents
- modernbert
- citation-context
base_model: answerdotai/ModernBERT-base
language:
- en
---
# SciEmbed-CTX
Signal A+B on a 7M-pair subsample (3 epochs). Best ablation; the FULL model is this recipe scaled to the full pool.
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
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("anon-nlp/sciembed-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 |
|---|---|---|---|---|
| 75.5 | **28.3** | 80.9 | 82.5 | 66.8 ± 0.02 |
## Citation
See the repository README. Paper: *SciEmbed: Citation-Context Supervision for Scientific Document Embeddings* (under review).
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