Datasets:
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
document-understanding
invoice
ocr
noise-augmentation
confidence-calibration
information-extraction
License:
File size: 5,310 Bytes
514b890 e2f4b13 514b890 e2f4b13 514b890 e2f4b13 514b890 e2f4b13 514b890 e2f4b13 514b890 e2f4b13 514b890 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | """
FCC Invoices Verified Augmented - Dataset Loader
Usage:
from huggingface_hub import snapshot_download
from load_dataset import FCCInvoicesDataset
local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")
ds = FCCInvoicesDataset(local_dir=local_dir)
# Iterate all (document, pipeline) pairs
for sample in ds:
print(sample["doc_id"], sample["pipeline_name"])
gt = sample["ground_truth"]["inference_result"]
print(gt["Agency"], gt["GrossTotal"])
# Get the local path to a single noisy PDF
path = ds.pdf_path(doc_id="033f718b16cb597c065930410752c294", pipeline_name="custom13")
Requirements:
pip install huggingface_hub
"""
import json
from pathlib import Path
from typing import Iterator
PIPELINES = [
"default", "archetype3", "archetype4", "archetype7", "archetype9",
"archetype10", "archetype11", "custom12", "custom13", "custom14",
"custom15", "custom16", "custom17", "custom18", "custom19",
"custom20", "custom21", "custom22",
]
class FCCInvoicesDataset:
"""Lazy iterator over all (document, pipeline) pairs in a local ConfBench checkout."""
def __init__(self, local_dir: str):
self.local_dir = Path(local_dir)
# Documents live under assets/; accept a checkout root or the assets dir itself.
if (self.local_dir / "assets").is_dir():
self.assets_dir = self.local_dir / "assets"
else:
self.assets_dir = self.local_dir
self._doc_ids: list[str] | None = None
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def doc_ids(self) -> list[str]:
"""Return all document IDs (md5 hashes)."""
if self._doc_ids is None:
self._doc_ids = sorted(
p.name for p in self.assets_dir.iterdir()
if p.is_dir() and (p / "metadata.json").exists()
)
return self._doc_ids
def load_ground_truth(self, doc_id: str) -> dict:
"""Read and parse gt.json for a document."""
return json.loads((self.assets_dir / doc_id / "gt.json").read_text())
def load_metadata(self, doc_id: str) -> dict:
"""Read and parse metadata.json (pipeline manifest) for a document."""
return json.loads((self.assets_dir / doc_id / "metadata.json").read_text())
def pdf_path(self, doc_id: str, pipeline_name: str) -> str:
"""Return the local path to a noisy PDF."""
return str(self.assets_dir / doc_id / pipeline_name / f"{pipeline_name}_noisy.pdf")
def original_pdf_path(self, doc_id: str) -> str:
"""Return the local path to the original clean PDF."""
return str(self.assets_dir / doc_id / "original.pdf")
def get_sample(self, doc_id: str, pipeline_name: str) -> dict:
"""Return a single sample dict."""
gt = self.load_ground_truth(doc_id)
return {
"doc_id": doc_id,
"pipeline_name": pipeline_name,
"pipeline_type": "archetype" if not pipeline_name.startswith("custom") else "custom",
"original_pdf": self.original_pdf_path(doc_id),
"noisy_pdf": self.pdf_path(doc_id, pipeline_name),
"ground_truth": gt,
}
def __iter__(self) -> Iterator[dict]:
"""Yield one dict per (document, pipeline) pair."""
for doc_id in self.doc_ids():
meta = self.load_metadata(doc_id)
gt = self.load_ground_truth(doc_id)
for pipeline in meta.get("pipelines", []):
name = pipeline["pipeline_name"]
yield {
"doc_id": doc_id,
"pipeline_name": name,
"pipeline_type": "archetype" if not name.startswith("custom") else "custom",
"original_pdf": self.original_pdf_path(doc_id),
"noisy_pdf": self.pdf_path(doc_id, name),
"ground_truth": gt,
}
def __len__(self) -> int:
return len(self.doc_ids()) * len(PIPELINES)
# ------------------------------------------------------------------
# CLI convenience
# ------------------------------------------------------------------
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="FCC Invoices dataset helper")
parser.add_argument("--local-dir", default=".", help="Path to local ConfBench checkout")
sub = parser.add_subparsers(dest="cmd")
p_list = sub.add_parser("list", help="List all document IDs")
p_gt = sub.add_parser("gt", help="Print ground truth for a document")
p_gt.add_argument("doc_id")
p_path = sub.add_parser("path", help="Print the local path to a noisy PDF")
p_path.add_argument("doc_id")
p_path.add_argument("pipeline_name")
args = parser.parse_args()
ds = FCCInvoicesDataset(local_dir=args.local_dir)
if args.cmd == "list":
for d in ds.doc_ids():
print(d)
elif args.cmd == "gt":
print(json.dumps(ds.load_ground_truth(args.doc_id), indent=2))
elif args.cmd == "path":
print(ds.pdf_path(args.doc_id, args.pipeline_name))
else:
parser.print_help()
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