Add files using upload-large-folder tool

.gitattributes

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README.md

@@ -23,50 +23,40 @@ tags:
   - kie
 dataset_info:
   features:
-    - name: doc_id
+    - name: id
       dtype: string
-    - name: pipeline_name
+    - name: noise_variant
       dtype: string
-    - name: original_pdf
-      dtype: string
-    - name: noisy_pdf
-      dtype: string
-    - name: ground_truth
+    - name: page_count
+      dtype: int32
+    - name: json_response
       dtype:
         struct:
-          - name: document_class
-            dtype:
+          - 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: type
+                - name: LineItemDescription
                   dtype: string
-          - name: inference_result
-            dtype:
-              struct:
-                - name: Agency
+                - name: LineItemStartDate
                   dtype: string
-                - name: Advertiser
+                - name: LineItemEndDate
                   dtype: string
-                - name: GrossTotal
-                  dtype: float64
-                - name: PaymentTerms
+                - name: LineItemDays
                   dtype: string
-                - name: AgencyCommission
+                - name: LineItemRate
                   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
@@ -87,29 +77,42 @@ Each document has:
 
 ## Quick Start
 
-```python
-from huggingface_hub import snapshot_download
+**Metadata + ground truth only** (fastest, no PDFs):
 
-# Download the full dataset locally
-local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")
-```
+```python
+from datasets import load_dataset
 
-Or clone with git:
+ds = load_dataset("amazon/ConfBench", data_files="data/test-00000-of-00001.parquet", split="train")
 
-```bash
-git clone https://huggingface.co/datasets/amazon/ConfBench
+for sample in ds:
+    print(sample["id"], sample["noise_variant"])
+    print(sample["json_response"]["Agency"])
 ```
 
-Or use the provided loader (see `load_dataset.py` in this release):
+**With PDFs** (downloads the full dataset locally):
 
 ```python
-from load_dataset import FCCInvoicesDataset
+import pyarrow.parquet as pq
+from huggingface_hub import snapshot_download
+from pathlib import Path
+
+local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
 
-ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")
+table = pq.read_table(local_dir / "data" / "test-00000-of-00001.parquet")
+df = table.to_pandas()
 
-for sample in ds:
-    print(sample["doc_id"], sample["pipeline_name"])
-    print(sample["ground_truth"]["inference_result"]["Agency"])
+# Access a PDF
+pdf_path = local_dir / "pdfs" / df["id"][0]
+
+# Access ground truth
+gt = df["json_response"][0]
+print(gt["Agency"], gt["GrossTotal"])
+```
+
+Or clone with git:
+
+```bash
+git clone https://huggingface.co/datasets/amazon/ConfBench
 ```
 
 ### Dataset Summary
@@ -132,19 +135,16 @@ for sample in ds:
 
 ```
 amazon/ConfBench/
-└── assets/
-    └── {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)
+├── data/
+│   └── test-00000-of-00001.parquet   # Metadata + ground truth (all variants)
+├── pdfs/
+│   ├── {doc_id}__original.pdf        # Original clean PDF
+│   ├── {doc_id}__default.pdf         # Default noise variant
+│   └── {doc_id}__{noise_variant}.pdf # Other noise variants
+
 ```
 
-### Ground Truth Schema (`gt.json`)
+### Ground Truth Schema (`json_response`)
 
 ```json
 {
@@ -174,20 +174,6 @@ amazon/ConfBench/
 }
 ```
 
-### 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
@@ -243,46 +229,6 @@ amazon/ConfBench/
 
 ---
 
-## 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"))
-assets_dir = local_dir / "assets"
-
-doc_ids = [p.name for p in assets_dir.iterdir() if p.is_dir()]
-
-# Load ground truth for a single document
-def load_gt(doc_id):
-    return json.loads((assets_dir / doc_id / "gt.json").read_text())
-
-# List noisy PDFs for a document
-def list_pipelines(doc_id):
-    meta = json.loads((assets_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:
@@ -301,7 +247,7 @@ The 75 clean FCC invoices used in this dataset are taken from [**RealKIE-FCC-Ver
 1. **Line Item Grouping** — fields previously treated as independent entries are grouped within each individual line item, aligning annotations with real-world invoice structure.
 2. **Annotation Corrections** — erroneous values in the original annotations were corrected.
 
-Starting from these 75 verified documents and their corrected `gt.json` ground truth, this dataset adds noise augmentation using the [Augraphy](https://github.com/sparkfish/augraphy) library with OCR-safe parameter settings, producing up to 18 degraded variants per document for confidence calibration and OCR robustness research.
+Starting from these 75 verified documents and their corrected ground truth (`json_response`), this dataset adds noise augmentation using the [Augraphy](https://github.com/sparkfish/augraphy) library with OCR-safe parameter settings, producing up to 18 degraded variants per document for confidence calibration and OCR robustness research.
 
 ---
 

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