Upload README.md with huggingface_hub

README.md

@@ -1,29 +1,60 @@
 ---
-dataset_info:
-  features:
-  - name: paper_id
-    dtype: string
-  - name: source
-    dtype: string
-  - name: title
-    dtype: string
-  - name: abstract
-    dtype: string
-  - name: num_figures
-    dtype: int32
-  - name: paper_json
-    dtype: string
-  - name: method_figure
-    dtype: image
-  - name: result_figure
-    dtype: image
-  splits:
-  - name: test
-    num_bytes: 212262589.24
-    num_examples: 1135
-  download_size: 187509505
-  dataset_size: 212262589.24
+license: cc-by-nc-4.0
+pretty_name: PaperGuard
+task_categories:
+- image-text-to-text
+- text-generation
+language:
+- en
+size_categories:
+- 1K<n<10K
+tags:
+- peer-review
+- adversarial-robustness
+- prompt-injection
+- multimodal
+- benchmark
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
 ---
-# Dataset Card for "PaperGuard"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# PaperGuard
+
+A benchmark of academic papers (text + key figures) for evaluating the robustness of
+multimodal AI peer-review systems.
+
+## Schema (single `test` split)
+
+| column | type | description |
+|---|---|---|
+| `paper_id` | string | paper identifier (e.g. `502`, `ICLR2020_10`, `1-26`) |
+| `source` | string | one of `iclr_2017`, `AgentReview`, `F1000` |
+| `title` | string | paper title (may be null) |
+| `abstract` | string | abstract text |
+| `num_figures` | int | number of figures present (0–2) |
+| `paper_json` | string | the full original parsed-paper JSON (`{"name", "metadata"}`), verbatim |
+| `method_figure` | image | the method figure (`<id>-1.png`), or null |
+| `result_figure` | image | the result figure (`<id>-2.png`), or null |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("rellabear/PaperGuard", split="test")
+row = ds[0]
+fig = row["method_figure"]   # PIL.Image or None
+```
+
+## License & Attribution
+
+Released for **non-commercial research** under **CC-BY-NC-4.0**. Please cite the original sources:
+
+- **F1000** — via [NLPeer](https://github.com/UKPLab/nlpeer) (Dycke et al., ACL 2023); F1000Research content is CC-BY.
+- **iclr_2017** — via [PeerRead](https://github.com/allenai/PeerRead) (Kang et al., NAACL 2018).
+- **AgentReview** — via [AgentReview](https://github.com/Ahren09/AgentReview) (Jin et al., EMNLP 2024).
+
+ICLR/OpenReview-derived content remains subject to [OpenReview's terms](https://openreview.net/legal/terms).