Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
File size: 6,934 Bytes
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 2: Create Benchmarks from Assets\n",
"\n",
"This notebook generates document splitting benchmarks using all strategies.\n",
"\n",
"**Prerequisites**: Assets created from notebook 01\n",
"\n",
"**Output**: Benchmark datasets with ground truth for train/test/validation splits"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../src/benchmarks')\n",
"\n",
"from services.asset_loader import AssetLoader\n",
"from services.split_manager import SplitManager\n",
"from services.benchmark_generator import BenchmarkGenerator\n",
"from services.benchmark_writer import BenchmarkWriter\n",
"from services.shuffle_strategies import get_strategy, STRATEGIES"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ASSETS_PATH = '../data/assets'\n",
"OUTPUT_PATH = '../data/benchmarks'\n",
"SPLIT_MAPPING_PATH = '../data/metadata/split_mapping.json'\n",
"\n",
"# Number of spliced documents per split\n",
"NUM_TRAIN = 800\n",
"NUM_TEST = 200\n",
"NUM_VAL = 500\n",
"\n",
"# Size: small (5-20 pages) or large (20-500 pages)\n",
"SIZE = 'small'\n",
"MIN_PAGES = 5 if SIZE == 'small' else 20\n",
"MAX_PAGES = 20 if SIZE == 'small' else 500\n",
"\n",
"RANDOM_SEED = 42\n",
"\n",
"print(f\"Configuration:\")\n",
"print(f\" Size: {SIZE} ({MIN_PAGES}-{MAX_PAGES} pages)\")\n",
"print(f\" Train: {NUM_TRAIN}, Test: {NUM_TEST}, Val: {NUM_VAL}\")\n",
"print(f\" Strategies: {list(STRATEGIES.keys())}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Assets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"loader = AssetLoader(assets_path=ASSETS_PATH)\n",
"documents_by_type = loader.load_all_documents()\n",
"\n",
"print(f\"Loaded {loader.total_documents} documents across {len(documents_by_type)} types\")\n",
"for doc_type, docs in documents_by_type.items():\n",
" print(f\" {doc_type}: {len(docs)} documents\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Train/Test/Validation Split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"split_manager = SplitManager(random_seed=RANDOM_SEED)\n",
"splits = split_manager.create_split(documents_by_type)\n",
"\n",
"# Save split mapping\n",
"split_manager.save_split(splits, SPLIT_MAPPING_PATH)\n",
"\n",
"print(f\"\\nSplit statistics:\")\n",
"for split_name in ['train', 'test', 'validation']:\n",
" total = sum(len(docs) for docs in splits[split_name].values())\n",
" print(f\" {split_name}: {total} documents\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate All Benchmarks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_strategies = ['mono_seq', 'mono_rand', 'poly_seq', 'poly_int', 'poly_rand']\n",
"\n",
"for strategy_name in all_strategies:\n",
" print(f\"\\n{'='*60}\")\n",
" print(f\"Processing strategy: {strategy_name}\")\n",
" print(f\"{'='*60}\")\n",
" \n",
" # Initialize strategy\n",
" strategy = get_strategy(\n",
" strategy_name,\n",
" min_pages=MIN_PAGES,\n",
" max_pages=MAX_PAGES,\n",
" random_seed=RANDOM_SEED\n",
" )\n",
" \n",
" generator = BenchmarkGenerator(strategy=strategy)\n",
" output_path = f'{OUTPUT_PATH}/{strategy_name}/{SIZE}'\n",
" writer = BenchmarkWriter(output_base_path=output_path, assets_path=ASSETS_PATH)\n",
" \n",
" # Generate train\n",
" print(f\"Generating train...\")\n",
" train_benchmark = generator.generate_for_split(\n",
" documents_by_type=documents_by_type,\n",
" doc_names_for_split=splits['train'],\n",
" num_spliced_docs=NUM_TRAIN,\n",
" split_name='train',\n",
" benchmark_name=strategy_name\n",
" )\n",
" writer.save_benchmark_set(train_benchmark, 'train')\n",
" \n",
" # Generate test\n",
" print(f\"Generating test...\")\n",
" test_benchmark = generator.generate_for_split(\n",
" documents_by_type=documents_by_type,\n",
" doc_names_for_split=splits['test'],\n",
" num_spliced_docs=NUM_TEST,\n",
" split_name='test',\n",
" benchmark_name=strategy_name\n",
" )\n",
" writer.save_benchmark_set(test_benchmark, 'test')\n",
" \n",
" # Generate validation\n",
" print(f\"Generating validation...\")\n",
" val_benchmark = generator.generate_for_split(\n",
" documents_by_type=documents_by_type,\n",
" doc_names_for_split=splits['validation'],\n",
" num_spliced_docs=NUM_VAL,\n",
" split_name='validation',\n",
" benchmark_name=strategy_name\n",
" )\n",
" writer.save_benchmark_set(val_benchmark, 'validation')\n",
" \n",
" print(f\"✅ Completed {strategy_name}\")\n",
"\n",
"print(f\"\\n{'='*60}\")\n",
"print(f\"✅ All benchmarks generated!\")\n",
"print(f\"{'='*60}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"\\n✅ Benchmark creation complete!\")\n",
"print(f\"\\nOutput structure:\")\n",
"for strategy_name in all_strategies:\n",
" print(f\"\\n{OUTPUT_PATH}/{strategy_name}/{SIZE}/\")\n",
" print(f\" ├── train.csv ({NUM_TRAIN} documents)\")\n",
" print(f\" ├── test.csv ({NUM_TEST} documents)\")\n",
" print(f\" ├── validation.csv ({NUM_VAL} documents)\")\n",
" print(f\" └── ground_truth_json/\")\n",
" print(f\" ├── train/ (JSON per document)\")\n",
" print(f\" ├── test/ (JSON per document)\")\n",
" print(f\" └── validation/ (JSON per document)\")\n",
"print(f\"\\nSplit mapping: {SPLIT_MAPPING_PATH}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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