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Datasets:
amazon
/
ConfBench

Tasks:
Document Question Answering
Image-to-Text
Modalities:
Document
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
document-understanding
invoice
ocr
noise-augmentation
confidence-calibration
information-extraction
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1

align-with-realkie-fcc-verified

#1
by sujimart - opened
base: refs/heads/main
←
from: refs/pr/1
Discussion Files changed
+1385
-37756
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Files changed (50) hide show
  1. .gitattributes +0 -0
  2. README.md +60 -114
  3. assets/033f718b16cb597c065930410752c294/gt.json +0 -91
  4. assets/033f718b16cb597c065930410752c294/metadata.json +0 -79
  5. assets/03f65053aea282ad8d5e759a9f18bdbb/gt.json +0 -27
  6. assets/03f65053aea282ad8d5e759a9f18bdbb/metadata.json +0 -87
  7. assets/08e2649c3291ee569a116260d46e9866/gt.json +0 -19
  8. assets/08e2649c3291ee569a116260d46e9866/metadata.json +0 -83
  9. assets/0d0fc87f34a2263cdd4b96cdac891240/gt.json +0 -420
  10. assets/0d0fc87f34a2263cdd4b96cdac891240/metadata.json +0 -87
  11. assets/1220d048d2a75d910c52e7f290e32ca0/gt.json +0 -101
  12. assets/1220d048d2a75d910c52e7f290e32ca0/metadata.json +0 -75
  13. assets/1449e9c1e67662cb3c806eea0b1771e1/gt.json +0 -171
  14. assets/1449e9c1e67662cb3c806eea0b1771e1/metadata.json +0 -71
  15. assets/1b238269de30dec67297e7ad8630116e/gt.json +0 -41
  16. assets/1b238269de30dec67297e7ad8630116e/metadata.json +0 -83
  17. assets/1b26192b674944e82347df9cfa3288ff/gt.json +0 -589
  18. assets/1b26192b674944e82347df9cfa3288ff/metadata.json +0 -75
  19. assets/21762bcc61029b7a5020647d7ced6aa5/gt.json +0 -34
  20. assets/21762bcc61029b7a5020647d7ced6aa5/metadata.json +0 -71
  21. assets/26372a567adab41e404cab267067d73f/gt.json +0 -62
  22. assets/26372a567adab41e404cab267067d73f/metadata.json +0 -79
  23. assets/2de731d64adfecacda0bf2e4d9a8fcfe/gt.json +0 -101
  24. assets/2de731d64adfecacda0bf2e4d9a8fcfe/metadata.json +0 -75
  25. assets/33fe0051cbf2e777e47a1e717091c61f/gt.json +0 -112
  26. assets/33fe0051cbf2e777e47a1e717091c61f/metadata.json +0 -71
  27. assets/3aac2d4d386a203e8d6419043f1adfa1/gt.json +0 -48
  28. assets/3aac2d4d386a203e8d6419043f1adfa1/metadata.json +0 -87
  29. assets/3b768c37cd4670c0f28b0e89d7507e05/gt.json +0 -178
  30. assets/3b768c37cd4670c0f28b0e89d7507e05/metadata.json +0 -75
  31. assets/3b9f33e058641d47cf2ebe76b9382648/gt.json +0 -19
  32. assets/3b9f33e058641d47cf2ebe76b9382648/metadata.json +0 -87
  33. assets/3d239b34e970a6f5ad4d1417352cf82e/gt.json +0 -141
  34. assets/3d239b34e970a6f5ad4d1417352cf82e/metadata.json +0 -75
  35. assets/3dabc131205d3a2c74a73eb545494d3f/gt.json +0 -49
  36. assets/3dabc131205d3a2c74a73eb545494d3f/metadata.json +0 -83
  37. assets/4d96667dff249bbfb513294f95313b71/gt.json +0 -507
  38. assets/4d96667dff249bbfb513294f95313b71/metadata.json +0 -75
  39. assets/52f1588f1bedc25f07c5cb6981e5f7dd/gt.json +0 -41
  40. assets/52f1588f1bedc25f07c5cb6981e5f7dd/metadata.json +0 -83
  41. assets/536c5c90c803552970cad769b6caef45/gt.json +0 -137
  42. assets/536c5c90c803552970cad769b6caef45/metadata.json +0 -75
  43. assets/5b79d718c8ac073a5b3c3ba2162173cb/gt.json +0 -623
  44. assets/5b79d718c8ac073a5b3c3ba2162173cb/metadata.json +0 -79
  45. assets/5cca3cc07f830f202928faa5a8f316af/gt.json +0 -327
  46. assets/5cca3cc07f830f202928faa5a8f316af/metadata.json +0 -71
  47. assets/64e40cb2f126bad63679f19505e8b0bf/gt.json +0 -49
  48. assets/64e40cb2f126bad63679f19505e8b0bf/metadata.json +0 -71
  49. assets/7429ba503d96718740c2f714598b40a4/gt.json +0 -431
  50. assets/7429ba503d96718740c2f714598b40a4/metadata.json +0 -75
.gitattributes CHANGED
The diff for this file is too large to render. See raw diff
 
README.md CHANGED
@@ -23,50 +23,40 @@ tags:
23
  - kie
24
  dataset_info:
25
  features:
26
- - name: doc_id
27
  dtype: string
28
- - name: pipeline_name
29
  dtype: string
30
- - name: original_pdf
31
- dtype: string
32
- - name: noisy_pdf
33
- dtype: string
34
- - name: ground_truth
35
  dtype:
36
  struct:
37
- - name: document_class
38
- dtype:
 
 
 
 
 
 
 
 
 
 
 
 
39
  struct:
40
- - name: type
41
  dtype: string
42
- - name: inference_result
43
- dtype:
44
- struct:
45
- - name: Agency
46
  dtype: string
47
- - name: Advertiser
48
  dtype: string
49
- - name: GrossTotal
50
- dtype: float64
51
- - name: PaymentTerms
52
  dtype: string
53
- - name: AgencyCommission
54
  dtype: float64
55
- - name: NetAmountDue
56
- dtype: float64
57
- - name: LineItems
58
- sequence:
59
- struct:
60
- - name: LineItemDescription
61
- dtype: string
62
- - name: LineItemStartDate
63
- dtype: string
64
- - name: LineItemEndDate
65
- dtype: string
66
- - name: LineItemDays
67
- dtype: string
68
- - name: LineItemRate
69
- dtype: float64
70
  splits:
71
  - name: test
72
  num_examples: 1346
@@ -87,29 +77,42 @@ Each document has:
87
 
88
  ## Quick Start
89
 
90
- ```python
91
- from huggingface_hub import snapshot_download
92
 
93
- # Download the full dataset locally
94
- local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")
95
- ```
96
 
97
- Or clone with git:
98
 
99
- ```bash
100
- git clone https://huggingface.co/datasets/amazon/ConfBench
 
101
  ```
102
 
103
- Or use the provided loader (see `load_dataset.py` in this release):
104
 
105
  ```python
106
- from load_dataset import FCCInvoicesDataset
 
 
 
 
107
 
108
- ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")
 
109
 
110
- for sample in ds:
111
- print(sample["doc_id"], sample["pipeline_name"])
112
- print(sample["ground_truth"]["inference_result"]["Agency"])
 
 
 
 
 
 
 
 
 
113
  ```
114
 
115
  ### Dataset Summary
@@ -132,19 +135,16 @@ for sample in ds:
132
 
133
  ```
134
  amazon/ConfBench/
135
- └── assets/
136
- └── {doc_id}/
137
- ├── original.pdf # Original clean PDF
138
- ├── gt.json # Ground truth annotations
139
- ├── metadata.json # Pipeline manifest
140
- ├── default/
141
- │ └── default_noisy.pdf
142
- ├── archetype3/
143
- │ └── archetype3_noisy.pdf
144
- └── ... (16 more pipelines)
145
  ```
146
 
147
- ### Ground Truth Schema (`gt.json`)
148
 
149
  ```json
150
  {
@@ -174,20 +174,6 @@ amazon/ConfBench/
174
  }
175
  ```
176
 
177
- ### Metadata Schema (`metadata.json`)
178
-
179
- ```json
180
- {
181
- "doc_hash": "033f718b16cb597c065930410752c294",
182
- "original_pdf": "original.pdf",
183
- "ground_truth": "gt.json",
184
- "pipelines": [
185
- {"pipeline_name": "default", "noisy_pdf": "default/default_noisy.pdf"},
186
- {"pipeline_name": "archetype3", "noisy_pdf": "archetype3/archetype3_noisy.pdf"}
187
- ]
188
- }
189
- ```
190
-
191
  ---
192
 
193
  ## Noise Pipelines
@@ -243,46 +229,6 @@ amazon/ConfBench/
243
 
244
  ---
245
 
246
- ## Usage
247
-
248
- ### Loading with huggingface_hub
249
-
250
- ```python
251
- from huggingface_hub import snapshot_download
252
- import json
253
- from pathlib import Path
254
-
255
- local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
256
- assets_dir = local_dir / "assets"
257
-
258
- doc_ids = [p.name for p in assets_dir.iterdir() if p.is_dir()]
259
-
260
- # Load ground truth for a single document
261
- def load_gt(doc_id):
262
- return json.loads((assets_dir / doc_id / "gt.json").read_text())
263
-
264
- # List noisy PDFs for a document
265
- def list_pipelines(doc_id):
266
- meta = json.loads((assets_dir / doc_id / "metadata.json").read_text())
267
- return meta["pipelines"]
268
- ```
269
-
270
- ### Using the Provided Loading Script
271
-
272
- ```python
273
- # See load_dataset.py in this release
274
- from load_dataset import FCCInvoicesDataset
275
-
276
- ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")
277
-
278
- # Iterate over all (doc, pipeline) pairs
279
- for sample in ds:
280
- print(sample["doc_id"], sample["pipeline_name"])
281
- print(sample["ground_truth"]["inference_result"]["Agency"])
282
- ```
283
-
284
- ---
285
-
286
  ## Intended Use
287
 
288
  This dataset is intended for:
@@ -301,7 +247,7 @@ The 75 clean FCC invoices used in this dataset are taken from [**RealKIE-FCC-Ver
301
  1. **Line Item Grouping** — fields previously treated as independent entries are grouped within each individual line item, aligning annotations with real-world invoice structure.
302
  2. **Annotation Corrections** — erroneous values in the original annotations were corrected.
303
 
304
- 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.
305
 
306
  ---
307
 
 
23
  - kie
24
  dataset_info:
25
  features:
26
+ - name: id
27
  dtype: string
28
+ - name: noise_variant
29
  dtype: string
30
+ - name: page_count
31
+ dtype: int32
32
+ - name: json_response
 
 
33
  dtype:
34
  struct:
35
+ - name: Agency
36
+ dtype: string
37
+ - name: Advertiser
38
+ dtype: string
39
+ - name: GrossTotal
40
+ dtype: float64
41
+ - name: PaymentTerms
42
+ dtype: string
43
+ - name: AgencyCommission
44
+ dtype: float64
45
+ - name: NetAmountDue
46
+ dtype: float64
47
+ - name: LineItems
48
+ sequence:
49
  struct:
50
+ - name: LineItemDescription
51
  dtype: string
52
+ - name: LineItemStartDate
 
 
 
53
  dtype: string
54
+ - name: LineItemEndDate
55
  dtype: string
56
+ - name: LineItemDays
 
 
57
  dtype: string
58
+ - name: LineItemRate
59
  dtype: float64
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
60
  splits:
61
  - name: test
62
  num_examples: 1346
 
77
 
78
  ## Quick Start
79
 
80
+ **Metadata + ground truth only** (fastest, no PDFs):
 
81
 
82
+ ```python
83
+ from datasets import load_dataset
 
84
 
85
+ ds = load_dataset("amazon/ConfBench", data_files="data/test-00000-of-00001.parquet", split="train")
86
 
87
+ for sample in ds:
88
+ print(sample["id"], sample["noise_variant"])
89
+ print(sample["json_response"]["Agency"])
90
  ```
91
 
92
+ **With PDFs** (downloads the full dataset locally):
93
 
94
  ```python
95
+ import pyarrow.parquet as pq
96
+ from huggingface_hub import snapshot_download
97
+ from pathlib import Path
98
+
99
+ local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))
100
 
101
+ table = pq.read_table(local_dir / "data" / "test-00000-of-00001.parquet")
102
+ df = table.to_pandas()
103
 
104
+ # Access a PDF
105
+ pdf_path = local_dir / "pdfs" / df["id"][0]
106
+
107
+ # Access ground truth
108
+ gt = df["json_response"][0]
109
+ print(gt["Agency"], gt["GrossTotal"])
110
+ ```
111
+
112
+ Or clone with git:
113
+
114
+ ```bash
115
+ git clone https://huggingface.co/datasets/amazon/ConfBench
116
  ```
117
 
118
  ### Dataset Summary
 
135
 
136
  ```
137
  amazon/ConfBench/
138
+ ├── data/
139
+ │ └── test-00000-of-00001.parquet # Metadata + ground truth (all variants)
140
+ ├── pdfs/
141
+ │ ├── {doc_id}__original.pdf # Original clean PDF
142
+ │ ├── {doc_id}__default.pdf # Default noise variant
143
+ │ └── {doc_id}__{noise_variant}.pdf # Other noise variants
144
+
 
 
 
145
  ```
146
 
147
+ ### Ground Truth Schema (`json_response`)
148
 
149
  ```json
150
  {
 
174
  }
175
  ```
176
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
177
  ---
178
 
179
  ## Noise Pipelines
 
229
 
230
  ---
231
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
232
  ## Intended Use
233
 
234
  This dataset is intended for:
 
247
  1. **Line Item Grouping** — fields previously treated as independent entries are grouped within each individual line item, aligning annotations with real-world invoice structure.
248
  2. **Annotation Corrections** — erroneous values in the original annotations were corrected.
249
 
250
+ 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.
251
 
252
  ---
253
 
assets/033f718b16cb597c065930410752c294/gt.json DELETED
@@ -1,91 +0,0 @@
1
- {
2
- "document_class": {
3
- "type": "Invoice"
4
- },
5
- "split_document": {
6
- "page_indices": [
7
- 0,
8
- 1
9
- ]
10
- },
11
- "inference_result": {
12
- "Agency": "American Media & Advocacy Group",
13
- "Advertiser": "National Rifle Association",
14
- "GrossTotal": 15185.0,
15
- "PaymentTerms": "30 Days",
16
- "AgencyCommission": 2277.75,
17
- "NetAmountDue": 12907.25,
18
- "LineItems": [
19
- {
20
- "LineItemDescription": "M-F 1135p-1205a",
21
- "LineItemStartDate": "10/09/12",
22
- "LineItemEndDate": "10/09/12",
23
- "LineItemDays": "-T-----",
24
- "LineItemRate": 600.0
25
- },
26
- {
27
- "LineItemDescription": "M-F 1135p-1205a",
28
- "LineItemStartDate": "10/09/12",
29
- "LineItemEndDate": "10/09/12",
30
- "LineItemDays": "-T-----",
31
- "LineItemRate": 600.0
32
- },
33
- {
34
- "LineItemDescription": "M-F 1135p-1205a",
35
- "LineItemStartDate": "10/10/12",
36
- "LineItemEndDate": "10/10/12",
37
- "LineItemDays": "--W----",
38
- "LineItemRate": 600.0
39
- },
40
- {
41
- "LineItemDescription": "M-F 1135p-1205a",
42
- "LineItemStartDate": "10/11/12",
43
- "LineItemEndDate": "10/11/12",
44
- "LineItemDays": "---T---",
45
- "LineItemRate": 600.0
46
- },
47
- {
48
- "LineItemDescription": "M-F 1135p-1205a",
49
- "LineItemStartDate": "10/12/12",
50
- "LineItemEndDate": "10/12/12",
51
- "LineItemDays": "----F--",
52
- "LineItemRate": 600.0
53
- },
54
- {
55
- "LineItemDescription": "Fox 6 Blitz",
56
- "LineItemStartDate": " 10/14/12",
57
- "LineItemEndDate": "10/14/12",
58
- "LineItemDays": "------S",
59
- "LineItemRate": 1000.0
60
- },
61
- {
62
- "LineItemDescription": "NFL on FOX 3p",
63
- "LineItemStartDate": "10/14/12",
64
- "LineItemEndDate": "10/14/12",
65
- "LineItemDays": "------S",
66
- "LineItemRate": 8350.0
67
- },
68
- {
69
- "LineItemDescription": "Su 1205p-1235a",
70
- "LineItemStartDate": "10/14/12",
71
- "LineItemEndDate": "10/14/12",
72
- "LineItemDays": "------S",
73
- "LineItemRate": 185.0
74
- },
75
- {
76
- "LineItemDescription": "College Football on",
77
- "LineItemStartDate": "10/13/12",
78
- "LineItemEndDate": "10/13/12",
79
- "LineItemDays": "-----S-",
80
- "LineItemRate": 1800.0
81
- },
82
- {
83
- "LineItemDescription": "Su 530-6p News",
84
- "LineItemStartDate": "10/14/12",
85
- "LineItemEndDate": "10/14/12",
86
- "LineItemDays": "------S",
87
- "LineItemRate": 850.0
88
- }
89
- ]
90
- }
91
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
assets/033f718b16cb597c065930410752c294/metadata.json DELETED
@@ -1,79 +0,0 @@
1
- {
2
- "doc_hash": "033f718b16cb597c065930410752c294",
3
- "original_pdf": "original.pdf",
4
- "ground_truth": "gt.json",
5
- "pipelines": [
6
- {
7
- "pipeline_name": "default",
8
- "noisy_pdf": "default/default_noisy.pdf"
9
- },
10
- {
11
- "pipeline_name": "archetype3",
12
- "noisy_pdf": "archetype3/archetype3_noisy.pdf"
13
- },
14
- {
15
- "pipeline_name": "archetype4",
16
- "noisy_pdf": "archetype4/archetype4_noisy.pdf"
17
- },
18
- {
19
- "pipeline_name": "archetype7",
20
- "noisy_pdf": "archetype7/archetype7_noisy.pdf"
21
- },
22
- {
23
- "pipeline_name": "archetype9",
24
- "noisy_pdf": "archetype9/archetype9_noisy.pdf"
25
- },
26
- {
27
- "pipeline_name": "archetype10",
28
- "noisy_pdf": "archetype10/archetype10_noisy.pdf"
29
- },
30
- {
31
- "pipeline_name": "archetype11",
32
- "noisy_pdf": "archetype11/archetype11_noisy.pdf"
33
- },
34
- {
35
- "pipeline_name": "custom12",
36
- "noisy_pdf": "custom12/custom12_noisy.pdf"
37
- },
38
- {
39
- "pipeline_name": "custom13",
40
- "noisy_pdf": "custom13/custom13_noisy.pdf"
41
- },
42
- {
43
- "pipeline_name": "custom14",
44
- "noisy_pdf": "custom14/custom14_noisy.pdf"
45
- },
46
- {
47
- "pipeline_name": "custom15",
48
- "noisy_pdf": "custom15/custom15_noisy.pdf"
49
- },
50
- {
51
- "pipeline_name": "custom16",
52
- "noisy_pdf": "custom16/custom16_noisy.pdf"
53
- },
54
- {
55
- "pipeline_name": "custom17",
56
- "noisy_pdf": "custom17/custom17_noisy.pdf"
57
- },
58
- {
59
- "pipeline_name": "custom18",
60
- "noisy_pdf": "custom18/custom18_noisy.pdf"
61
- },
62
- {
63
- "pipeline_name": "custom19",
64
- "noisy_pdf": "custom19/custom19_noisy.pdf"
65
- },
66
- {
67
- "pipeline_name": "custom20",
68
- "noisy_pdf": "custom20/custom20_noisy.pdf"
69
- },
70
- {
71
- "pipeline_name": "custom21",
72
- "noisy_pdf": "custom21/custom21_noisy.pdf"
73
- },
74
- {
75
- "pipeline_name": "custom22",
76
- "noisy_pdf": "custom22/custom22_noisy.pdf"
77
- }
78
- ]
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- "pipeline_name": "custom15",
40
- "noisy_pdf": "custom15/custom15_noisy.pdf"
41
- },
42
- {
43
- "pipeline_name": "custom16",
44
- "noisy_pdf": "custom16/custom16_noisy.pdf"
45
- },
46
- {
47
- "pipeline_name": "custom17",
48
- "noisy_pdf": "custom17/custom17_noisy.pdf"
49
- },
50
- {
51
- "pipeline_name": "custom18",
52
- "noisy_pdf": "custom18/custom18_noisy.pdf"
53
- },
54
- {
55
- "pipeline_name": "custom19",
56
- "noisy_pdf": "custom19/custom19_noisy.pdf"
57
- },
58
- {
59
- "pipeline_name": "custom20",
60
- "noisy_pdf": "custom20/custom20_noisy.pdf"
61
- },
62
- {
63
- "pipeline_name": "custom21",
64
- "noisy_pdf": "custom21/custom21_noisy.pdf"
65
- },
66
- {
67
- "pipeline_name": "custom22",
68
- "noisy_pdf": "custom22/custom22_noisy.pdf"
69
- },
70
- {
71
- "pipeline_name": "custom23",
72
- "noisy_pdf": "custom23/custom23_noisy.pdf"
73
- }
74
- ]
75
- }