Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- feature-extraction
+- sentence-similarity
+language:
+- en
+tags:
+- scientific-documents
+- long-context
+- retrieval
+- citation-context
+pretty_name: Body-Fact Retrieval (BFR) diagnostic
+size_categories:
+- 1K<n<10K
+---
+
+# Body-Fact Retrieval (BFR) diagnostic
+
+A science-specific long-context retrieval probe released with **SciEmbed**
+(*Citation-Context Supervision for Scientific Document Embeddings*, Findings of
+EMNLP 2026). The task: given a sentence drawn from the **body** of a scientific
+paper that does not appear in its abstract, retrieve the source paper from a
+pool of full-text documents. Short-context encoders that only see the
+title+abstract are blind to the content that must be matched; long-context
+encoders that read the body can recover it, so the probe isolates whether a
+model's context window is actually exploited on scientific text.
+
+## Contents
+
+- `candidates.parquet` — 9,749 candidate papers (`candidate_id`, `title`,
+  `abstract`, `body`, `field_of_study`, `citation_count`).
+- `queries.parquet` — 1,000 body-sentence queries (`query_id`, `query_text`,
+  `gold_candidate_id`).
+
+## Construction
+
+- **Queries** are single sentences from the middle third of each paper
+  (skipping intro/conclusion), 80–400 characters, with at most 0.2 trigram
+  overlap against the paper's abstract so the abstract alone is not a shortcut.
+- **Leakage control.** Every candidate paper has a citation count in [1, 4],
+  strictly below the citation_count ≥ 5 threshold used to build the SciEmbed
+  Stage-2 (Signal A/B) training pool. No candidate was a training anchor,
+  positive, or hard negative.
+- **Coverage.** Roughly 400 papers per field across ~24 S2AG disciplines
+  (Computer Science, Mathematics, Physics, Biology, Medicine, Law, History,
+  Philosophy, …). Bodies span ~11k–59k characters (5th–95th percentile).
+
+## Usage
+
+Encode each query and each candidate (title + abstract + body, up to the
+model's context length), then retrieve by cosine similarity and report
+Recall@k. See the SciEmbed repository for the full evaluation harness and the
+robustness variants (same-field pool, Qwen3-paraphrased queries).
+
+- Code: https://github.com/J0nasW/SciEmbed-release
+- Models: https://huggingface.co/J0nasW
+
+## Citation
+
+```bibtex
+@inproceedings{wilinski2026sciembed,
+  title={SciEmbed: Citation-Context Supervision for Scientific Document Embeddings},
+  author={Wilinski, Jonas and F{\"a}rber, Michael},
+  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2026},
+  year={2026}
+}
+```