Create README.md

README.md

@@ -0,0 +1,52 @@
+---
+license: apache-2.0
+task_categories:
+- question-answering
+language:
+- en
+---
+
+This is the Repo for ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus.
+
+The paper of ViDoRAG is available at [arXiv](https://arxiv.org/abs/2502.18017).
+
+ViDoSeek sets itself apart with its heightened difficulty level, attributed to the multi-document context and the intricate nature of its content types, particularly the Layout category. The dataset contains both single-hop and multi-hop queries, presenting a diverse set of challenges.
+
+We have also released the SlideVQA dataset, refined through our pipeline, which we refer to as SlideVQA-Refined. This dataset is suitable for evaluating retrieval-augmented generation tasks as well.
+
+
+The annotation is in the form of a JSON file.
+```json
+{
+    "uid": "04d8bb0db929110f204723c56e5386c1d8d21587_2",
+    // Unique identifier to distinguish different queries
+    "query": "What is the temperature of Steam explosion of Pretreatment for Switchgrass and Sugarcane bagasse preparation?", 
+    // Query content
+    "reference_answer": "195-205 Centigrade", 
+    // Reference answer to the query
+    "meta_info": {
+        "file_name": "Pretreatment_of_Switchgrass.pdf", 
+        // Original file name, typically a PDF file
+        "reference_page": [10, 11], 
+        // Reference page numbers represented as an array
+        "source_type": "Text", 
+        // Type of data source, 2d_layout\Text\Table\Chart
+        "query_type": "Multi-Hop" 
+        // Query type, Multi-Hop or Single-Hop
+    }
+}
+```
+
+
+If you find this dataset useful, please consider citing our paper:
+```bigquery
+@misc{wang2025vidoragvisualdocumentretrievalaugmented,
+      title={ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents}, 
+      author={Qiuchen Wang and Ruixue Ding and Zehui Chen and Weiqi Wu and Shihang Wang and Pengjun Xie and Feng Zhao},
+      year={2025},
+      eprint={2502.18017},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.18017}, 
+}
+```