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README.md

@@ -27,7 +27,6 @@ configs:
   - split: train
     path: card_samples/parquet/qa.parquet
 ---
-
 # KnowCP Dataset Repository
 
 Project homepage and benchmark details:
@@ -56,67 +55,35 @@ The following counts are aligned with the benchmark website configuration and pu
 
 ## Repository Contents
 
-1. images
-  All painting images and related sub-images used by the benchmark.
-
-2. questions
-  All benchmark QA files. We provide 14 question files under [hf_repo/questions/by_type](hf_repo/questions/by_type), and each file corresponds to one question source type.
-
-  All QA content is in Chinese.
-  For concrete QA presentation style and English-facing examples, see:
-  http://localhost:5173/#question-distribution
-
-  14 question files and counts used in the website benchmark view:
-
-  - ITT_MHQA_choice.jsonl: 4232
-  - ITT_MHQA_fillin.jsonl: 4232
-  - MITT_MHQA_choice.jsonl: 608
-  - MITT_MHQA_fillin.jsonl: 608
-  - TTI.jsonl: 1210
-  - SR.jsonl: 1792
-  - IR.jsonl: 2351
-  - ER_choice.jsonl: 11359
-  - ER_fillin.jsonl: 5052
-  - TR_choice.jsonl: 1312
-  - TR_fillin.jsonl: 1312
-  - VA.jsonl: 922
-  - CC.jsonl: 922
-  - PR.jsonl: 922
-
-  Simple task descriptions, following the website organization:
-
-  Foundational Knowledge
-  - ITT and MITT style tasks: identify title-level information from one image or multiple images.
-  - TTI: retrieve the matching image from title information.
-  - MHQA: multi-step reasoning over image and context, provided in choice and fill-in formats.
-
-  Visual Content
-  - SR: seal recognition.
-  - IR: inscription or colophon recognition.
-  - ER: element recognition, provided in multiple-choice and fill-in formats.
-  - TR: painting technique recognition, provided in multiple-choice and fill-in formats.
-
-  Deep Reasoning
-  - VA: visual analysis.
-  - CC: cultural context reasoning.
-  - PR: provenance research reasoning.
-
-3. annotations
-  Fine-grained annotations produced by our annotators for each painting image, including seals, inscriptions, elements, and techniques.
-
-  Annotation visualization references:
-  http://localhost:5173/#distribution
-
-4. kb
-  Core metadata per painting, including identity and background attributes used by benchmark tasks.
-
-## Folder Reference
-
-- [hf_repo/images](hf_repo/images): all images
-- [hf_repo/questions](hf_repo/questions): question sets
-- [hf_repo/annotations](hf_repo/annotations): fine-grained annotations
-- [hf_repo/kb](hf_repo/kb): painting metadata knowledge base
-- [hf_repo/mappings](hf_repo/mappings): mapping resources used in processing and alignment
+1. **images** : All painting images and related sub-images used by the benchmark.
+2. **questions** : All benchmark QA files. We provide 14 question files under **questions/by_type** and each file corresponds to one question source type.
+
+   * All QA content is in Chinese. For concrete QA presentation style and English-facing examples, see:http://localhost:5173/#question-distribution
+   * 14 question files and counts used in the website benchmark view:
+     * **Foundational Knowledge**
+       * ITT and MITT style tasks(1210): identify title-level information from one image or multiple images.
+
+         * ITT is in **questions/by_type/ITT_MHQA_choice.json** and **questions/by_type/ITT_MHQA_fillin.json**
+         * MITT is in **questions/by_type/MITT_MHQA_choice.json** and **questions/by_type/MITT_MHQA_fillin.json**
+       * TTI(1210): retrieve the matching image from title information.
+       * MHQA(4840): multi-step reasoning over image and context, provided in choice and fill-in formats.
+
+         * MHQA is in **questions/by_type/ITT_MHQA_choice.json** , **questions/by_type/ITT_MHQA_fillin.json , questions/by_type/MITT_MHQA_choice.json** and **questions/by_type/MITT_MHQA_fillin.json**
+     * **Visual Content**
+       * SR(1792): seal recognition.
+       * IR(2351): inscription or colophon recognition.
+       * ER(16411): element recognition, provided in multiple-choice and fill-in formats.
+       * TR(2624): painting technique recognition, provided in multiple-choice and fill-in formats.
+     * **Deep Reasoning**
+       * VA(922): visual analysis.
+       * CC(922): cultural context reasoning.
+       * PR(922): provenance research reasoning.
+3. **annotations**
+   Fine-grained annotations produced by our annotators for each painting image, including seals, inscriptions, elements, and techniques.
+
+   * Annotation visualization references : http://localhost:5173/#distribution
+4. **kb**
+   Core metadata per painting, including identity and background attributes used by benchmark tasks.
 
 ## If You Want to Run Evaluation