Update README.md

README.md

@@ -122,6 +122,161 @@ pip install -r requirements.txt
 
 ---
 
+## Usage
+
+### Step 1: Create Assets
+
+Convert raw PDFs into structured assets with page images (300 DPI PNG) and OCR text (Markdown).
+
+#### Option A: AWS Textract OCR (Default)
+
+Best for English documents. Processes all document categories with Textract.
+
+```bash
+python src/assets/run.py \
+  --raw-data-path data/raw_data \
+  --output-path data/assets \
+  --s3-bucket your-bucket-name \
+  --s3-prefix textract-temp \
+  --workers 10 \
+  --save-mapping
+```
+
+**Requirements:**
+- AWS credentials configured (`aws configure`)
+- S3 bucket for temporary file uploads
+- No GPU required
+
+#### Option B: Hybrid OCR (Textract + DeepSeek)
+
+Uses Textract for most categories, DeepSeek OCR only for the "language" category (multilingual documents).
+
+**Note:** For this project, DeepSeek OCR was used only for the "language" category and executed in AWS SageMaker AI with GPU instances (e.g., `ml.g6.xlarge`).
+
+**1. Install flash-attention (Required for DeepSeek):**
+
+```bash
+# For CUDA 12.x with Python 3.12:
+cd /mnt/sagemaker-nvme  # Use larger disk for downloads
+wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
+pip install flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
+
+# For other CUDA/Python versions: https://github.com/Dao-AILab/flash-attention/releases
+```
+
+**2. Set cache directory (Important for SageMaker):**
+
+```bash
+# SageMaker: Use larger NVMe disk instead of small home directory
+export HF_HOME=/mnt/sagemaker-nvme/cache
+export TRANSFORMERS_CACHE=/mnt/sagemaker-nvme/cache
+```
+
+**3. Run asset creation:**
+
+```bash
+python src/assets/run.py \
+  --raw-data-path data/raw_data \
+  --output-path data/assets \
+  --s3-bucket your-bucket-name \
+  --use-deepseek-for-language \
+  --workers 10 \
+  --save-mapping
+```
+
+**Requirements:**
+- NVIDIA GPU with CUDA support (tested on ml.g6.xlarge)
+- ~10GB+ disk space for model downloads
+- flash-attention library installed
+- AWS credentials (for Textract on non-language categories)
+- S3 bucket (for Textract on non-language categories)
+
+**How it works:**
+- Documents in `raw_data/language/` → DeepSeek OCR (GPU)
+- All other categories → AWS Textract (cloud)
+
+#### Parameters
+
+- `--raw-data-path`: Directory containing source PDFs organized by document type
+- `--output-path`: Where to save extracted assets (images + OCR text)
+- `--s3-bucket`: S3 bucket name (required for Textract)
+- `--s3-prefix`: S3 prefix for temporary files (default: textract-temp)
+- `--workers`: Number of parallel processes (default: 10)
+- `--save-mapping`: Save CSV mapping document IDs to file paths
+- `--use-deepseek-for-language`: Use DeepSeek OCR for "language" category only
+- `--limit`: Process only N documents (useful for testing)
+
+#### What Happens
+
+1. Scans `raw_data/` directory for PDFs organized by document type
+2. Extracts each page as 300 DPI PNG image
+3. Runs OCR (Textract or DeepSeek) to extract text
+4. Saves structured assets in `output-path/{doc_type}/{doc_name}/`
+5. Optionally creates `document_mapping.csv` listing all processed documents
+6. These assets become the input for Step 2 (benchmark generation)
+
+#### Output Structure
+
+```
+data/assets/
+└── {doc_type}/{filename}/
+    ├── original/{filename}.pdf
+    └── pages/{page_num}/
+        ├── page-{num}.png          # 300 DPI image
+        └── page-{num}-textract.md  # OCR text
+```
+
+
+
+## Interactive Notebooks
+
+Explore the toolkit with Jupyter notebooks:
+
+1. **`notebooks/01_create_assets.ipynb`** - Create assets from PDFs
+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
+
+Each benchmark JSON contains:
+
+```json
+{
+  "benchmark_name": "poly_seq",
+  "strategy": "PolySeq",
+  "split": "train",
+  "created_at": "2026-01-30T12:00:00",
+  "documents": [
+    {
+      "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
+    }
+  ],
+  "statistics": {
+    "total_spliced_documents": 1000,
+    "total_pages": 7500,
+    "unique_doc_types": 16
+  }
+}
+```
+
+## Requirements
+
+- Python 3.8+
+- AWS credentials (for Textract OCR)
+- Dependencies: `boto3`, `loguru`, `pymupdf`, `pillow`
+
+---
+
 ### Generate Benchmark Datasets
 
 ```bash
@@ -240,111 +395,7 @@ doc-split-benchmark/
     └── benchmarks/          # Generated benchmarks
 ```
 
-## Usage
-
-### Step 1: Create Assets
-
-Convert raw PDFs into structured assets with page images (300 DPI PNG) and OCR text (Markdown).
-
-#### Option A: AWS Textract OCR (Default)
-
-Best for English documents. Processes all document categories with Textract.
-
-```bash
-python src/assets/run.py \
-  --raw-data-path data/raw_data \
-  --output-path data/assets \
-  --s3-bucket your-bucket-name \
-  --s3-prefix textract-temp \
-  --workers 10 \
-  --save-mapping
-```
-
-**Requirements:**
-- AWS credentials configured (`aws configure`)
-- S3 bucket for temporary file uploads
-- No GPU required
-
-#### Option B: Hybrid OCR (Textract + DeepSeek)
-
-Uses Textract for most categories, DeepSeek OCR only for the "language" category (multilingual documents).
-
-**Note:** For this project, DeepSeek OCR was used only for the "language" category and executed in AWS SageMaker AI with GPU instances (e.g., `ml.g6.xlarge`).
-
-**1. Install flash-attention (Required for DeepSeek):**
-
-```bash
-# For CUDA 12.x with Python 3.12:
-cd /mnt/sagemaker-nvme  # Use larger disk for downloads
-wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
-pip install flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
-
-# For other CUDA/Python versions: https://github.com/Dao-AILab/flash-attention/releases
-```
-
-**2. Set cache directory (Important for SageMaker):**
-
-```bash
-# SageMaker: Use larger NVMe disk instead of small home directory
-export HF_HOME=/mnt/sagemaker-nvme/cache
-export TRANSFORMERS_CACHE=/mnt/sagemaker-nvme/cache
-```
-
-**3. Run asset creation:**
-
-```bash
-python src/assets/run.py \
-  --raw-data-path data/raw_data \
-  --output-path data/assets \
-  --s3-bucket your-bucket-name \
-  --use-deepseek-for-language \
-  --workers 10 \
-  --save-mapping
-```
-
-**Requirements:**
-- NVIDIA GPU with CUDA support (tested on ml.g6.xlarge)
-- ~10GB+ disk space for model downloads
-- flash-attention library installed
-- AWS credentials (for Textract on non-language categories)
-- S3 bucket (for Textract on non-language categories)
-
-**How it works:**
-- Documents in `raw_data/language/` → DeepSeek OCR (GPU)
-- All other categories → AWS Textract (cloud)
-
-#### Parameters
-
-- `--raw-data-path`: Directory containing source PDFs organized by document type
-- `--output-path`: Where to save extracted assets (images + OCR text)
-- `--s3-bucket`: S3 bucket name (required for Textract)
-- `--s3-prefix`: S3 prefix for temporary files (default: textract-temp)
-- `--workers`: Number of parallel processes (default: 10)
-- `--save-mapping`: Save CSV mapping document IDs to file paths
-- `--use-deepseek-for-language`: Use DeepSeek OCR for "language" category only
-- `--limit`: Process only N documents (useful for testing)
-
-#### What Happens
-
-1. Scans `raw_data/` directory for PDFs organized by document type
-2. Extracts each page as 300 DPI PNG image
-3. Runs OCR (Textract or DeepSeek) to extract text
-4. Saves structured assets in `output-path/{doc_type}/{doc_name}/`
-5. Optionally creates `document_mapping.csv` listing all processed documents
-6. These assets become the input for Step 2 (benchmark generation)
-
-#### Output Structure
-
-```
-data/assets/
-└── {doc_type}/{filename}/
-    ├── original/{filename}.pdf
-    └── pages/{page_num}/
-        ├── page-{num}.png          # 300 DPI image
-        └── page-{num}-textract.md  # OCR text
-```
-
-### Step 2: Generate Benchmarks
+### Generate Benchmarks [Detailed]
 
 Create DocSplit benchmarks with train/test/validation splits.
 
@@ -390,53 +441,6 @@ data/
             └── validation.csv
 ```
 
-## Interactive Notebooks
-
-Explore the toolkit with Jupyter notebooks:
-
-1. **`notebooks/01_create_assets.ipynb`** - Create assets from PDFs
-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
-
-Each benchmark JSON contains:
-
-```json
-{
-  "benchmark_name": "poly_seq",
-  "strategy": "PolySeq",
-  "split": "train",
-  "created_at": "2026-01-30T12:00:00",
-  "documents": [
-    {
-      "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
-    }
-  ],
-  "statistics": {
-    "total_spliced_documents": 1000,
-    "total_pages": 7500,
-    "unique_doc_types": 16
-  }
-}
-```
-
-## Requirements
-
-- Python 3.8+
-- AWS credentials (for Textract OCR)
-- Dependencies: `boto3`, `loguru`, `pymupdf`, `pillow`
-
 
 # How to cite this dataset