---
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-FULL

Headline model. Stage 1 DAPT + contrastive on the ~30M-pair Signal A+B pool (1 epoch). The SciRepEval number in the paper is the mean over three seeds; this repo ships the seed-123 weights.

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-full")
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 |
|---|---|---|---|---|
| 75.6 | 28.2 | 80.9 | 82.7 | **66.85 ± 0.38** |

## Citation

See the repository README. Paper: *SciEmbed: Citation-Context Supervision for Scientific Document Embeddings* (under review).