init

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+pretrained_Economics.json filter=lfs diff=lfs merge=lfs -text
+pretrained_Geology.json filter=lfs diff=lfs merge=lfs -text
+pretrained_Mathematics.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,28 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# Dataset Details
+
+This dataset contains pretrained data constructed from academic text-rich graphs ([MAPLE](https://github.com/yuzhimanhua/MAPLE)) spanning three disciplines: Economics, Mathematics, and Geology. It is used for pretraining [RAMP](https://arxiv.org/abs/2603.14937) (Raw-text Anchored Message Passing), which recasts the LLM as a graph-native aggregation operator on text-rich graphs.
+
+The dataset includes the following files:
+
+- `pretrained_Economics.json`
+- `pretrained_Mathematics.json`
+- `pretrained_Geology.json`
+
+This is a release from our paper [LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs](https://arxiv.org/abs/2603.14937), so please cite it if using this dataset.
+
+# Citation
+
+```bibtex
+@article{zhang2026llm,
+  title={LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs},
+  author={Zhang, Ying and Yu, Hang and Zhang, Haipeng and Di, Peng},
+  journal={arXiv preprint arXiv:2603.14937},
+  year={2026}
+}
+```
+
+[![Paper](https://img.shields.io/badge/arXiv-2603.14937-b31b1b.svg)](https://arxiv.org/abs/2603.14937)

pretrained_Economics.json

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+size 1433772977

pretrained_Geology.json

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+size 2237095081

pretrained_Mathematics.json

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