Update docs to reference HF hosting instead of private S3 bucket

README.md

@@ -1,3 +1,332 @@
 ---
+language:
+  - en
 license: cc-by-nc-4.0
+task_categories:
+  - document-question-answering
+  - image-to-text
+task_ids:
+  - document-understanding
+  - information-extraction
+pretty_name: FCC Invoices Verified Augmented
+size_categories:
+  - 1K<n<10K
+annotations_creators:
+  - expert-generated
+source_datasets:
+  - https://indicodatasolutions.github.io/RealKIE/
+tags:
+  - document-understanding
+  - invoice
+  - ocr
+  - noise-augmentation
+  - confidence-calibration
+  - information-extraction
+  - kie
+dataset_info:
+  features:
+    - name: doc_id
+      dtype: string
+    - name: pipeline_name
+      dtype: string
+    - name: original_pdf
+      dtype: string
+    - name: noisy_pdf
+      dtype: string
+    - name: ground_truth
+      dtype:
+        struct:
+          - name: document_class
+            dtype:
+              struct:
+                - name: type
+                  dtype: string
+          - name: inference_result
+            dtype:
+              struct:
+                - name: Agency
+                  dtype: string
+                - name: Advertiser
+                  dtype: string
+                - name: GrossTotal
+                  dtype: float64
+                - name: PaymentTerms
+                  dtype: string
+                - name: AgencyCommission
+                  dtype: float64
+                - name: NetAmountDue
+                  dtype: float64
+                - name: LineItems
+                  sequence:
+                    struct:
+                      - name: LineItemDescription
+                        dtype: string
+                      - name: LineItemStartDate
+                        dtype: string
+                      - name: LineItemEndDate
+                        dtype: string
+                      - name: LineItemDays
+                        dtype: string
+                      - name: LineItemRate
+                        dtype: float64
+  splits:
+    - name: test
+      num_examples: 1346
 ---
+
+# FCC Invoices Verified Augmented
+
+## Dataset Description
+
+**FCC Invoices Verified Augmented** is a document understanding benchmark dataset consisting of 75 real-world Federal Communications Commission (FCC) invoice documents, each augmented with up to 18 distinct document degradation pipelines. The dataset is designed to support confidence calibration research, OCR robustness evaluation, and key information extraction (KIE) under realistic noise conditions.
+
+Each document has:
+- A clean original PDF
+- Up to 18 noisy versions generated by distinct Augraphy-based degradation pipelines
+- Verified ground-truth entity annotations
+
+**Total samples**: 1,346 (75 documents × up to 18 noise pipelines)
+
+## Quick Start
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download the full dataset locally
+local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")
+```
+
+Or clone with git:
+
+```bash
+git clone https://huggingface.co/datasets/amazon/ConfBench
+```
+
+Or use the provided loader (see `load_dataset.py` in this release):
+
+```python
+from load_dataset import FCCInvoicesDataset
+
+ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")
+
+for sample in ds:
+    print(sample["doc_id"], sample["pipeline_name"])
+    print(sample["ground_truth"]["inference_result"]["Agency"])
+```
+
+### Dataset Summary
+
+| Property | Value |
+|---|---|
+| Domain | Legal / Broadcast Advertising |
+| Document Type | FCC Invoice (multi-page PDF) |
+| # Base Documents | 75 |
+| # Noise Pipelines | up to 18 per document |
+| # Total Samples | 1,346 |
+| Avg Pages per Doc | ~2 |
+| License | CC BY-NC 4.0 |
+
+---
+
+## Dataset Structure
+
+### Repository Layout
+
+```
+amazon/ConfBench/
+└── {doc_id}/
+    ├── original.pdf              # Original clean PDF
+    ├── gt.json                   # Ground truth annotations
+    ├── metadata.json             # Pipeline manifest
+    ├── default/
+    │   └── default_noisy.pdf
+    ├── archetype3/
+    │   └── archetype3_noisy.pdf
+    └── ... (16 more pipelines)
+```
+
+### Ground Truth Schema (`gt.json`)
+
+```json
+{
+  "document_class": {
+    "type": "Invoice"
+  },
+  "split_document": {
+    "page_indices": [0, 1]
+  },
+  "inference_result": {
+    "Agency": "American Media & Advocacy Group",
+    "Advertiser": "National Rifle Association",
+    "GrossTotal": 15185.0,
+    "PaymentTerms": "30 Days",
+    "AgencyCommission": 2277.75,
+    "NetAmountDue": 12907.25,
+    "LineItems": [
+      {
+        "LineItemDescription": "M-F 1135p-1205a",
+        "LineItemStartDate": "10/09/12",
+        "LineItemEndDate": "10/09/12",
+        "LineItemDays": "-T-----",
+        "LineItemRate": 600.0
+      }
+    ]
+  }
+}
+```
+
+### Metadata Schema (`metadata.json`)
+
+```json
+{
+  "doc_hash": "033f718b16cb597c065930410752c294",
+  "original_pdf": "original.pdf",
+  "ground_truth": "gt.json",
+  "pipelines": [
+    {"pipeline_name": "default", "noisy_pdf": "default/default_noisy.pdf"},
+    {"pipeline_name": "archetype3", "noisy_pdf": "archetype3/archetype3_noisy.pdf"}
+  ]
+}
+```
+
+---
+
+## Noise Pipelines
+
+18 distinct Augraphy-based degradation pipelines covering a wide range of real-world scan/print artifacts:
+
+### Augraphy Archetypes (pre-built pipelines)
+
+| Pipeline | Description |
+|---|---|
+| `default` | Balanced general-purpose degradation |
+| `archetype3` | Heavy post-processing effects |
+| `archetype4` | Minimal geometric distortions |
+| `archetype7` | Color and lighting variations |
+| `archetype9` | Texture-based degradations |
+| `archetype10` | Scanner artifact simulation |
+| `archetype11` | Complex multi-phase pipeline |
+
+### Custom Pipelines (research-designed)
+
+| Pipeline | Key Augmentations | Simulates |
+|---|---|---|
+| `custom12` | DirtyDrum + DirtyRollers | Scanner roller artifacts |
+| `custom13` | Stains + Folding | Physical document damage |
+| `custom14` | BleedThrough + InkMottling | Ink bleed and mottling |
+| `custom15` | Moire + ColorPaper | Scanning/aging effects |
+| `custom16` | ShadowCast + LightingGradient | Uneven lighting |
+| `custom17` | Jpeg + SubtleNoise | Compression artifacts |
+| `custom18` | Geometric + PageBorder | Alignment issues |
+| `custom19` | BindingsAndFasteners + Letterpress | Binding shadows |
+| `custom20` | Brightness + BadPhotoCopy | Photocopy quality |
+| `custom21` | WaterMark + NoisyLines | Overlaid artifacts |
+| `custom22` | Dithering + DotMatrix | Dot-matrix printing |
+
+---
+
+## Entity Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `Agency` | string | Advertising agency name |
+| `Advertiser` | string | Client/advertiser name |
+| `GrossTotal` | float | Total invoice amount (USD) |
+| `PaymentTerms` | string | Payment terms (e.g., "30 Days") |
+| `AgencyCommission` | float | Agency commission amount (USD) |
+| `NetAmountDue` | float | Net amount after commission (USD) |
+| `LineItems` | list | Individual line items |
+| `LineItemDescription` | string | Program/slot description |
+| `LineItemStartDate` | string | Airing start date (MM/DD/YY) |
+| `LineItemEndDate` | string | Airing end date (MM/DD/YY) |
+| `LineItemDays` | string | Days of week pattern (e.g., "-T-----") |
+| `LineItemRate` | float | Cost per line item (USD) |
+
+---
+
+## Usage
+
+### Loading with huggingface_hub
+
+```python
+from huggingface_hub import snapshot_download
+import json
+from pathlib import Path
+
+local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
+
+doc_ids = [p.name for p in local_dir.iterdir() if p.is_dir()]
+
+# Load ground truth for a single document
+def load_gt(doc_id):
+    return json.loads((local_dir / doc_id / "gt.json").read_text())
+
+# List noisy PDFs for a document
+def list_pipelines(doc_id):
+    meta = json.loads((local_dir / doc_id / "metadata.json").read_text())
+    return meta["pipelines"]
+```
+
+### Using the Provided Loading Script
+
+```python
+# See load_dataset.py in this release
+from load_dataset import FCCInvoicesDataset
+
+ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")
+
+# Iterate over all (doc, pipeline) pairs
+for sample in ds:
+    print(sample["doc_id"], sample["pipeline_name"])
+    print(sample["ground_truth"]["inference_result"]["Agency"])
+```
+
+---
+
+## Intended Use
+
+This dataset is intended for:
+
+1. **Confidence calibration research** — Measuring model confidence under varying degrees of document degradation.
+2. **OCR robustness evaluation** — Benchmarking OCR and KIE systems on realistic noisy documents.
+3. **Document understanding** — Evaluating models on structured information extraction from invoices (evaluation/benchmark use only; no training split is provided).
+4. **Noise impact analysis** — Studying how specific noise types affect extraction accuracy per field.
+
+---
+
+## Source Data
+
+The original clean FCC invoices are derived from the [RealKIE benchmark](https://indicodatasolutions.github.io/RealKIE/) (FCC Invoices subset). Ground truth annotations were verified and the documents augmented using the [Augraphy](https://github.com/sparkfish/augraphy) library with OCR-safe parameter settings.
+
+---
+
+## How to Cite This Dataset
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{islam2026confidencecalibration,
+  title     = {FCC Invoices Verified Augmented: A Benchmark for Confidence Calibration in Document Understanding under Noise},
+  author    = {Md Mofijul Islam and Mohammad Rostami and others},
+  year      = {2026},
+  note      = {Dataset available at Hugging Face},
+  url       = {https://huggingface.co/datasets/amazon/ConfBench}
+}
+```
+
+Also cite the original RealKIE dataset:
+
+```bibtex
+@inproceedings{realkie2023,
+  title  = {RealKIE: Five Novel Datasets for Key Information Extraction},
+  author = {Indico Data Solutions},
+  year   = {2023},
+  url    = {https://indicodatasolutions.github.io/RealKIE/}
+}
+```
+
+---
+
+## License
+
+This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). The original FCC invoice documents are public records.