Datasets:

HenryExcellent
/

SciDocBench

@@ -18,82 +18,53 @@ configs:
 # SciDocBench
-Official data release for [SciDocBench: A Workflow-Centered Benchmark and Data
-Pipeline for Scientific Document Understanding](https://arxiv.org/abs/2609.05141).
-Project and evaluation code: [InternLM/SciDocBench](https://github.com/InternLM/SciDocBench).
-## Dataset Summary
-SciDocBench contains 496 evaluation instances derived from
-124 expert-authored scientific-document questions.
-Each question is represented under four matched settings that cross English/Chinese
-questions with all-images-first/interleaved document representations.
-| Partition | Instances |
-|---|---:|
-| EN, all images first | 124 |
-| EN, interleaved | 124 |
-| ZH, all images first | 124 |
-| ZH, interleaved | 124 |
-The release references 7,052 images across instances and
-contains 2,758 unique image files after SHA-256 deduplication.
-## Files
-- `data/test-00000-of-00001.parquet`: Hugging Face test split.
-- `SciDocBench.tsv`: portable VLMEvalKit-compatible source table.
-- `scidocbench.tsv`: compatibility alias for the original repository path.
-- `images/SciDocBench/`: content-addressed document images.
-- `release_manifest.json`: counts and checksums.
-- `manifests/images.jsonl`: per-image hashes and reference counts.
-In the Parquet file, `image_path` is a list of paths relative to the VLMEvalKit image
-root, while `images` contains repository-relative paths. Interleaved `segments` retain
-their ordering and refer to the rewritten `image_path` values.
-## Usage
-Load the table with Hugging Face Datasets:
 ```python
 from datasets import load_dataset
-dataset = load_dataset("HenryExcellent/SciDocBench", split="test")
 ```
-For a local VLMEvalKit checkout, download the repository and place the TSV and image
-tree under `LMUData`:
-```bash
-hf download HenryExcellent/SciDocBench   --repo-type dataset   --local-dir SciDocBench
-cp SciDocBench/SciDocBench.tsv "$LMUData/SciDocBench.tsv"
-mkdir -p "$LMUData/images/SciDocBench"
-cp -a SciDocBench/images/SciDocBench/. "$LMUData/images/SciDocBench/"
-```
 ## Evaluation
-Use the SciDocBench integration in
-[VLMEvalKit](https://github.com/open-compass/VLMEvalKit) for standardized inference
-and evaluation.
-## License and Use
-Benchmark annotations and scripts are released for research use under the terms stated
-by the SciDocBench project. Paper pages and figures originate from heterogeneous
-scientific sources and may retain source-specific copyright or license terms. Users
-are responsible for checking the applicable terms before redistribution or commercial
-use. No blanket license is asserted over third-party document imagery.
-## Citation
-```bibtex
-@article{wu2026scidocbench,
-  title   = {SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding},
-  author  = {Wu, Shenxi and Liu, Yuhong and Zhang, Haosong and Zou, Tongjin and Zhang, Yanxun and Chen, Gaochang and Liang, Dun and Wang, Jiaqi and Wang, Zhecan James and Zang, Yuhang and Lin, Dahua},
-  journal = {arXiv preprint arXiv:2609.05141},
-  year    = {2026}
-}
-```

 # SciDocBench
+Scientific document understanding benchmark with expert-authored questions,
+reference answers, source images, and four matched evaluation settings.
+## Contents
+There are 124 underlying questions and 496 evaluation instances across English
+and Chinese, each paired with all-images-first and interleaved document inputs.
+The release includes 2,758 distinct images and 7,052 image references.
+- `SciDocBench.tsv`: portable VLMEvalKit-compatible benchmark.
+- `data/test-00000-of-00001.parquet`: the same questions as a Hugging Face test split.
+- `images/SciDocBench/`: content-addressed images.
+- `manifests/images.jsonl`: image hashes and reference counts.
+- `release_manifest.json`: data counts and checksums.
+The TSV and Parquet files are two representations of the same benchmark, not
+different question sets.
+## Offline Usage
+Download the files through the anonymous repository interface. Place
+`SciDocBench.tsv` under your VLMEvalKit `LMUData` directory and copy
+`images/SciDocBench/` into `LMUData/images/SciDocBench/`.
+The TSV `image_path` entries are relative to the VLMEvalKit image root;
+Parquet `images` entries are relative to this repository. Keep interleaved
+segments in their recorded order.
+For local tabular analysis:
 ```python
 from datasets import load_dataset
+benchmark = load_dataset("parquet", data_files={"test": "data/test-00000-of-00001.parquet"}, split="test")
 ```
+Do not use these evaluation questions for training.
 ## Evaluation
+Use the SciDocBench integration from the anonymous supporting-code repository.
+The question-ID dispatch determines the evaluator: 57 underlying questions use
+deterministic rules, 66 semantic judgment, and one execution. Internal reasoning
+is excluded from formal final-answer scoring.
+## Source Materials and License
+Paper pages and figures retain their source-specific copyright and license
+terms. Authors named inside the source papers are part of the benchmark inputs,
+not attribution of this submission. No blanket license is asserted over
+third-party document imagery. Users must check applicable source terms before
+redistribution or commercial use.

release_manifest.json CHANGED Viewed

@@ -50,7 +50,6 @@
   },
   "files": {
     "vlmevalkit_tsv": "SciDocBench.tsv",
-    "legacy_tsv_alias": "scidocbench.tsv",
     "parquet": "data/test-00000-of-00001.parquet",
     "image_root": "images/SciDocBench"
   },

   },
   "files": {
     "vlmevalkit_tsv": "SciDocBench.tsv",
     "parquet": "data/test-00000-of-00001.parquet",
     "image_root": "images/SciDocBench"
   },

scidocbench.tsv DELETED Viewed

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