Intermediate long-context variant (max_seq_length=2048). Point on the 512→2K→8K context-length scan.
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-ctx-2048")
emb = model.encode(["citation-context supervision for scientific embeddings"],
normalize_embeddings=True)
Context length:
2048 tokens
Pooling:
mean ·
Output dim:
768 (Matryoshka-truncatable to 512/256/128)
License:
MIT
Citation
See the repository README. Paper:
SciEmbed: Citation-Context Supervision for Scientific Document Embeddings
(under review).
Runs of anon-nlp sciembed-ctx-2048 on huggingface.co
100
Total runs
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24-hour runs
3
3-day runs
7
7-day runs
-13
30-day runs
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