anon-nlp / sciembed-ctx-8192

huggingface.co
Total runs: 84
24-hour runs: -15
7-day runs: 3
30-day runs: -5
Model's Last Updated: June 08 2026
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Introduction of sciembed-ctx-8192

Model Details of sciembed-ctx-8192

SciEmbed-CTX-8192

Long-context variant (max_seq_length=8192). Recommended for long scientific inputs; best within-recipe scores on the Body-Fact Retrieval probe and LongEmbed.

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-8192")
emb = model.encode(["citation-context supervision for scientific embeddings"],
                   normalize_embeddings=True)
  • Context length: 8192 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-8192 on huggingface.co

84
Total runs
-15
24-hour runs
-1
3-day runs
3
7-day runs
-5
30-day runs

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