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README.md

@@ -10,6 +10,35 @@ language:
 pretty_name: olmOCR-bench Pre-Rendered
 size_categories:
 - 1K<n<10K
+configs:
+- config_name: arxiv_math
+  data_files:
+  - split: test
+    path: images/arxiv_math/**
+- config_name: headers_footers
+  data_files:
+  - split: test
+    path: images/headers_footers/**
+- config_name: long_tiny_text
+  data_files:
+  - split: test
+    path: images/long_tiny_text/**
+- config_name: multi_column
+  data_files:
+  - split: test
+    path: images/multi_column/**
+- config_name: old_scans
+  data_files:
+  - split: test
+    path: images/old_scans/**
+- config_name: old_scans_math
+  data_files:
+  - split: test
+    path: images/old_scans_math/**
+- config_name: tables
+  data_files:
+  - split: test
+    path: images/tables/**
 ---
 
 # olmOCR-bench Pre-Rendered
@@ -24,13 +53,36 @@ This dataset eliminates that setup entirely by hosting the pre-rendered images d
 
 All files are accessible via direct URL, so you can evaluate any model by just pointing at these URLs — no local downloads, no PDF rendering tools, no dataset cloning.
 
+## Dataset Structure
+
+The dataset has **7 subsets** (one per benchmark category), each with a **test** split:
+
+| Subset | PDFs | Tests | Test Types |
+|--------|------|-------|------------|
+| `arxiv_math` | 522 | 2,927 | math |
+| `headers_footers` | 266 | 753 | absent |
+| `long_tiny_text` | 62 | 442 | present |
+| `multi_column` | 231 | 884 | order |
+| `old_scans` | 98 | 526 | present, absent, order |
+| `old_scans_math` | 36 | 458 | math |
+| `tables` | 188 | 1,020 | table |
+
+### Loading with `datasets`
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("shhdwi/olmocr-pre-rendered", "arxiv_math", split="test")
+print(ds[0])  # {'image': <PIL.Image>, 'pdf_stem': '...', 'category': '...', ...}
+```
+
 ## Contents
 
-| Directory | Contents | Count | Size |
-|-----------|----------|-------|------|
-| `ground_truth/` | JSONL test case files (7 categories, from allenai/olmOCR-bench) | 7,010 tests | ~2.4 MB |
-| `images/` | Pre-rendered PNG images (page 1 of each PDF, 2048px longest dim) | 1,403 images | ~1 GB |
-| `predictions/` | Published model prediction caches | 1,403 .md files | ~7.5 MB |
+| Directory | Contents | Count |
+|-----------|----------|-------|
+| `images/` | Pre-rendered PNG images (page 1, 2048px longest dim) with `metadata.jsonl` per category | 1,403 images |
+| `ground_truth/` | JSONL test case files (from allenai/olmOCR-bench) | 7,010 tests |
+| `predictions/` | Published model prediction caches | 1,403 .md files |
 
 ## Rendering Details
 
@@ -39,7 +91,7 @@ Each PDF page is rendered to PNG matching the official olmOCR benchmark process:
 - **Resolution**: `target_longest_image_dim = 2048` (longest side scaled to 2048px, aspect ratio preserved)
 - **Renderer**: PyMuPDF (same pixel output as pdftoppm used in the official repo)
 - **Pages**: Page 1 only (the benchmark tests only page 1 of each PDF)
-- **Naming**: `{pdf_stem}_pg1.png` (e.g., `2503.05390_pg14_pg1.png`)
+- **Naming**: `{pdf_stem}_pg1.png`
 
 This matches what the official benchmark runners do internally:
 - `run_chatgpt.py`: `render_pdf_to_base64png(pdf_path, target_longest_image_dim=2048)`
@@ -75,18 +127,6 @@ https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered/resolve/main/images/a
 https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered/resolve/main/ground_truth/arxiv_math.jsonl
 ```
 
-## Categories
-
-| Category | PDFs | Tests | Test Types |
-|----------|------|-------|------------|
-| arxiv_math | 522 | 2,927 | math |
-| headers_footers | 266 | 753 | absent |
-| long_tiny_text | 62 | 442 | present |
-| multi_column | 231 | 884 | order |
-| old_scans | 98 | 526 | present, absent, order |
-| old_scans_math | 36 | 458 | math |
-| table_tests | 188 | 1,020 | table |
-
 ## Attribution
 
 Based on [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench) by Allen AI ([paper](https://huggingface.co/papers/2502.18443)). Licensed ODC-BY-1.0.