Datasets:
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
document-understanding
invoice
ocr
noise-augmentation
confidence-calibration
information-extraction
License:
Update layout docs for assets/ restructure
Browse files
README.md
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```
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amazon/ConfBench/
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└──
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```
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### Ground Truth Schema (`gt.json`)
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from pathlib import Path
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local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
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doc_ids = [p.name for p in
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# Load ground truth for a single document
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def load_gt(doc_id):
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return json.loads((
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# List noisy PDFs for a document
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def list_pipelines(doc_id):
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meta = json.loads((
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return meta["pipelines"]
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```
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```
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amazon/ConfBench/
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└── assets/
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└── {doc_id}/
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├── original.pdf # Original clean PDF
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├── gt.json # Ground truth annotations
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├── metadata.json # Pipeline manifest
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├── default/
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│ └── default_noisy.pdf
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├── archetype3/
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│ └── archetype3_noisy.pdf
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└── ... (16 more pipelines)
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```
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### Ground Truth Schema (`gt.json`)
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from pathlib import Path
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local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
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assets_dir = local_dir / "assets"
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doc_ids = [p.name for p in assets_dir.iterdir() if p.is_dir()]
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# Load ground truth for a single document
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def load_gt(doc_id):
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return json.loads((assets_dir / doc_id / "gt.json").read_text())
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# List noisy PDFs for a document
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def list_pipelines(doc_id):
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meta = json.loads((assets_dir / doc_id / "metadata.json").read_text())
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return meta["pipelines"]
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```
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