fix: Revise README for accuracy, usability, and dependency compatibility

README.md

@@ -18,6 +18,32 @@ size_categories:
 
 **In addition to the dataset, we release this repository containing the complete toolkit for generating the benchmark datasets, along with Jupyter notebooks for data analysis.**
 
+## Quick Start: Load the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load all splits
+ds = load_dataset("amazon/doc_split")
+
+# Or load a single split
+test = load_dataset("amazon/doc_split", split="test")
+```
+
+Each row represents a spliced document packet:
+
+```python
+doc = ds["train"][0]
+print(doc["doc_id"])              # UUID for this packet
+print(doc["total_pages"])         # Total pages in the packet
+print(len(doc["subdocuments"]))   # Number of constituent documents
+
+for sub in doc["subdocuments"]:
+    print(f"  {sub['doc_type_id']}: {len(sub['page_ordinals'])} pages")
+```
+
+> **Note:** The `image_path` and `text_path` fields in each page reference assets that are not included in the dataset download. See [Data Formats](#data-formats) for details.
+
 # DocSplit: Document Packet Splitting Benchmark Generator
 
 A toolkit for creating benchmark datasets to test document packet splitting systems. Document packet splitting is the task of separating concatenated multi-page documents into individual documents with correct page ordering.
@@ -30,6 +56,26 @@ This toolkit generates five benchmark datasets of varying complexity to test how
 2. **Classify document types** accurately  
 3. **Reconstruct correct page ordering** within each document
 
+## Dataset Schema
+
+When loaded via `load_dataset()`, each row contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `doc_id` | string | UUID identifying the spliced packet |
+| `total_pages` | int | Total number of pages in the packet |
+| `subdocuments` | list | Array of constituent documents |
+
+Each subdocument contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `doc_type_id` | string | Document type category |
+| `local_doc_id` | string | Identifier within the packet |
+| `group_id` | string | Group identifier |
+| `page_ordinals` | list[int] | Page positions within the packet |
+| `pages` | list | Per-page metadata (image_path, text_path, original_doc_name) |
+
 ## Document Source
 
 We uses the documents from **RVL-CDIP-N-MP**:  
@@ -128,8 +174,12 @@ pip install -r requirements.txt
 
 Convert raw PDFs into structured assets with page images (300 DPI PNG) and OCR text (Markdown).
 
+> **Note:** The code defaults for `--raw-data-path` (`../raw_data`) and `--output-path` (`../processed_assets`) assume running from within `src/assets/`. When running from the repo root, pass explicit paths as shown below.
+
 #### Option A: AWS Textract OCR (Default)
 
+> **⚠️ Requires Python 3.12:** This command uses `amazon-textract-textractor`, which has C extension dependencies that may not build on Python 3.13+. See [Requirements](#requirements).
+
 Best for English documents. Processes all document categories with Textract.
 
 ```bash
@@ -236,48 +286,76 @@ Explore the toolkit with Jupyter notebooks:
 2. **`notebooks/02_create_benchmarks.ipynb`** - Generate benchmarks with different strategies
 3. **`notebooks/03_analyze_benchmarks.ipynb`** - Analyze and visualize benchmark statistics
 
-## Benchmark Output Format
+## Data Formats
+
+The dataset provides two complementary formats for each benchmark:
+
+### Ground Truth JSON (used by `load_dataset`)
 
-Each benchmark JSON contains:
+One JSON file per document packet in `datasets/{strategy}/{size}/ground_truth_json/{split}/`:
 
 ```json
 {
-  "benchmark_name": "poly_seq",
-  "strategy": "PolySeq",
-  "split": "train",
-  "created_at": "2026-01-30T12:00:00",
-  "documents": [
+  "doc_id": "...",
+  "total_pages": ...,
+  "subdocuments": [
     {
-      "spliced_doc_id": "splice_0001",
-      "source_documents": [
-        {"doc_type": "invoice", "doc_name": "doc1", "pages": [1,2,3]},
-        {"doc_type": "letter", "doc_name": "doc2", "pages": [1,2]}
-      ],
-      "ground_truth": [
-        {"page_num": 1, "doc_type": "invoice", "source_doc": "doc1", "source_page": 1},
-        {"page_num": 2, "doc_type": "invoice", "source_doc": "doc1", "source_page": 2},
-        ...
-      ],
-      "total_pages": 5
+      "doc_type_id": "...",
+      "local_doc_id": "...",
+      "group_id": "...",
+      "page_ordinals": [...],
+      "pages": [
+        {
+          "page": 1,
+          "original_doc_name": "...",
+          "image_path": "rvl-cdip-nmp-assets/...",
+          "text_path": "rvl-cdip-nmp-assets/...",
+          "local_doc_id_page_ordinal": ...
+        }
+      ]
     }
-  ],
-  "statistics": {
-    "total_spliced_documents": 1000,
-    "total_pages": 7500,
-    "unique_doc_types": 16
-  }
+  ]
 }
 ```
 
+### CSV (flat row-per-page format)
+
+One CSV per split in `datasets/{strategy}/{size}/`:
+
+| Column | Description |
+|--------|-------------|
+| `doc_type` | Document type category |
+| `original_doc_name` | Source document filename |
+| `parent_doc_name` | UUID of the spliced packet (matches `doc_id` in JSON) |
+| `local_doc_id` | Local identifier within the packet |
+| `page` | Page number within the packet |
+| `image_path` | Path to page image (prefix: `data/assets/`) |
+| `text_path` | Path to OCR text (prefix: `data/assets/`) |
+| `group_id` | Group identifier |
+| `local_doc_id_page_ordinal` | Page ordinal within the original source document |
+
+### Asset Paths
+
+The image and text paths in both formats reference assets that are **not included** in this repository:
+
+- JSON paths use prefix `rvl-cdip-nmp-assets/`
+- CSV paths use prefix `data/assets/`
+
+To resolve these paths, run the asset creation pipeline (see [Create Assets](#step-1-create-assets)). The data can be used for metadata and label analysis without the actual images.
+
 ## Requirements
 
-- Python 3.8+
+- Python 3.12+ recommended (see note below)
 - AWS credentials (for Textract OCR)
-- Dependencies: `boto3`, `loguru`, `pymupdf`, `pillow`
+- Dependencies: `pip install -r requirements.txt`
+
+> **⚠️ Python Version:** The `amazon-textract-textractor` package (required by `src/assets/run.py`) depends on C extensions (`editdistance`) that may fail to build on Python 3.13+. **Python 3.12 is recommended.** Using [uv](https://docs.astral.sh/uv/) as your package installer can also help resolve build issues.
+
+> **Note:** `requirements.txt` currently includes GPU dependencies (PyTorch, Transformers) that are only needed for DeepSeek OCR on multilingual documents. If you only need Textract OCR or want to explore the pre-generated data, the core dependencies are: `boto3`, `loguru`, `pymupdf`, `pillow`, `pydantic`, `amazon-textract-textractor`, `tenacity`.
 
 ---
 
-### Generate Benchmark Datasets
+### Download Source Data and Generate Benchmarks
 
 ```bash
 # 1. Download and extract RVL-CDIP-N-MP source data from HuggingFace (1.25 GB)
@@ -292,7 +370,7 @@ cd ../..
 
 # 2. Create assets from raw PDFs
 #    Extracts each page as PNG image and runs OCR to get text
-#    These assets are then used in step 4 to create benchmark datasets
+#    These assets are then used in step 3 to create benchmark datasets
 #    Output: Structured assets in data/assets/ with images and text per page
 python src/assets/run.py --raw-data-path data/raw_data --output-path data/assets
 
@@ -353,46 +431,73 @@ The toolkit generates five benchmarks of increasing complexity, based on the Doc
 - **Challenge**: Worst-case scenario with no structural assumptions
 - **Use Case**: Document management system failures or emergency recovery
 
+### Dataset Statistics
+
+The pre-generated benchmarks include train, test, and validation splits in both `small` (5–20 pages per packet) and `large` (20–500 pages per packet) sizes. For `mono_rand/large`:
+
+| Split | Document Count |
+|-------|---------------|
+| Train | 417 |
+| Test | 96 |
+| Validation | 51 |
 
 ## Project Structure
 
 ```
 doc-split-benchmark/
 ├── README.md
-├── requirements.txt           # All dependencies
+├── requirements.txt
 ├── src/
-│   ├── assets/               # Asset creation from PDFs
-│   │   ├── run.py           # Main script
-│   │   ├── models.py        # Document models
+│   ├── assets/                    # Asset creation from PDFs
+│   │   ├── __init__.py
+│   │   ├── models.py
+│   │   ├── run.py                 # Main entry point
 │   │   └── services/
+│   │       ├── __init__.py
+│   │       ├── asset_creator.py
+│   │       ├── asset_writer.py
+│   │       ├── deepseek_ocr.py
 │   │       ├── pdf_loader.py
-│   │       ├── textract_ocr.py
-│   │       └── asset_writer.py
+│   │       └── textract_ocr.py
 │   │
-│   └── benchmarks/           # Benchmark generation
-│       ├── run.py           # Main script
-│       ├── models.py        # Benchmark models
+│   └── benchmarks/                # Benchmark generation
+│       ├── __init__.py
+│       ├── models.py
+│       ├── run.py                 # Main entry point
 │       └── services/
+│           ├── __init__.py
 │           ├── asset_loader.py
 │           ├── split_manager.py
 │           ├── benchmark_generator.py
 │           ├── benchmark_writer.py
-│           └── strategies/
-│               ├── mono_seq.py    # DocSplit-Mono-Seq
-│               ├── mono_rand.py   # DocSplit-Mono-Rand
-│               ├── poly_seq.py    # DocSplit-Poly-Seq
-│               ├── poly_int.py    # DocSplit-Poly-Int
-│               └── poly_rand.py   # DocSplit-Poly-Rand
+│           └── shuffle_strategies/
+│               ├── __init__.py
+│               ├── base_strategy.py
+│               ├── mono_seq.py
+│               ├── mono_rand.py
+│               ├── poly_seq.py
+│               ├── poly_int.py
+│               └── poly_rand.py
 │
-├── notebooks/                # Interactive examples
+├── notebooks/
 │   ├── 01_create_assets.ipynb
 │   ├── 02_create_benchmarks.ipynb
 │   └── 03_analyze_benchmarks.ipynb
 │
-└── data/                     # Generated data (not in repo)
-    ├── raw_data/            # Downloaded PDFs
-    ├── assets/              # Extracted images + OCR
-    └── benchmarks/          # Generated benchmarks
+├── datasets/                      # Pre-generated benchmark data
+│   └── {strategy}/{size}/
+│       ├── train.csv
+│       ├── test.csv
+│       ├── validation.csv
+│       └── ground_truth_json/
+│           ├── train/*.json
+│           ├── test/*.json
+│           └── validation/*.json
+│
+└── data/                          # Generated by toolkit (not in repo)
+    ├── raw_data/
+    ├── assets/
+    └── benchmarks/
 ```
 
 ### Generate Benchmarks [Detailed]
@@ -405,8 +510,8 @@ python src/benchmarks/run.py \
   --assets-path data/assets \
   --output-path data/benchmarks \
   --num-docs-train 800 \
-  --num-docs-test 200 \
-  --num-docs-val 500 \
+  --num-docs-test 500 \
+  --num-docs-val 200 \
   --size small \
   --random-seed 42
 ```
@@ -415,9 +520,9 @@ python src/benchmarks/run.py \
 - `--strategy`: Benchmark strategy - `mono_seq`, `mono_rand`, `poly_seq`, `poly_int`, `poly_rand`, or `all` (default: all)
 - `--assets-path`: Directory containing assets from Step 1 (default: data/assets)
 - `--output-path`: Where to save benchmarks (default: data/benchmarks)
-- `--num-docs-train`: Number of spliced documents for training (default: 8)
-- `--num-docs-test`: Number of spliced documents for testing (default: 5)
-- `--num-docs-val`: Number of spliced documents for validation (default: 2)
+- `--num-docs-train`: Number of spliced documents for training (default: 800)
+- `--num-docs-test`: Number of spliced documents for testing (default: 500)
+- `--num-docs-val`: Number of spliced documents for validation (default: 200)
 - `--size`: Benchmark size - `small` (5-20 pages) or `large` (20-500 pages) (default: small)
 - `--split-mapping`: Path to split mapping JSON (default: data/metadata/split_mapping.json)
 - `--random-seed`: Seed for reproducibility (default: 42)