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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
id: int64
section: string
path: string
source_section: string
umap_x: double
topic_id: int64
document_id: int64
umap_y: double
to
{'document_id': Value('int64'), 'source_section': Value('string'), 'topic_id': Value('int64'), 'umap_x': Value('float64'), 'umap_y': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: int64
section: string
path: string
source_section: string
umap_x: double
topic_id: int64
document_id: int64
umap_y: double
to
{'document_id': Value('int64'), 'source_section': Value('string'), 'topic_id': Value('int64'), 'umap_x': Value('float64'), 'umap_y': Value('float64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1925, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
document_id int64 | source_section string | topic_id int64 | umap_x float64 | umap_y float64 |
|---|---|---|---|---|
83,775 | cia_declassified | -1 | -0.670368 | 9.131941 |
83,785 | cia_declassified | -1 | -0.649409 | 9.134406 |
83,801 | cia_declassified | 33 | -0.305014 | 9.320644 |
83,803 | cia_declassified | 33 | -0.413938 | 9.314631 |
83,806 | cia_declassified | -1 | -0.421169 | 9.614262 |
83,808 | cia_declassified | -1 | -0.396392 | 9.637251 |
83,813 | cia_declassified | -1 | -0.490672 | 9.70668 |
83,816 | cia_declassified | -1 | -0.496882 | 9.720457 |
83,823 | cia_declassified | -1 | -0.50234 | 9.736053 |
83,826 | cia_declassified | -1 | -0.49509 | 9.712523 |
83,836 | cia_declassified | -1 | -0.501081 | 9.701585 |
83,839 | cia_declassified | -1 | -0.504774 | 9.718281 |
83,847 | cia_declassified | -1 | -0.525954 | 9.739817 |
83,849 | cia_declassified | -1 | -0.515453 | 9.715253 |
83,860 | cia_declassified | -1 | -0.450104 | 9.569947 |
83,869 | cia_declassified | -1 | -0.455768 | 9.626823 |
83,875 | cia_declassified | -1 | -0.487644 | 9.707845 |
83,877 | cia_declassified | -1 | -0.495672 | 9.714231 |
83,893 | cia_declassified | -1 | -0.994581 | 10.177147 |
83,896 | cia_declassified | -1 | -0.575917 | 9.757483 |
83,899 | cia_declassified | -1 | -0.787926 | 9.114765 |
83,901 | cia_declassified | -1 | -0.784556 | 9.114153 |
83,903 | cia_declassified | -1 | -0.807466 | 9.081636 |
83,904 | cia_declassified | -1 | -0.781423 | 9.092244 |
83,934 | cia_declassified | 33 | -0.37123 | 9.373391 |
83,940 | cia_declassified | 33 | -0.375514 | 9.383495 |
83,969 | cia_declassified | 33 | -0.335249 | 9.437382 |
83,976 | cia_declassified | 33 | -0.329797 | 9.422871 |
84,000 | cia_declassified | -1 | -0.754373 | 9.853019 |
84,021 | cia_declassified | -1 | -0.8067 | 9.589862 |
84,028 | cia_declassified | -1 | -0.489094 | 9.582432 |
84,030 | cia_declassified | -1 | -0.528478 | 9.639404 |
84,034 | cia_declassified | -1 | -0.865502 | 10.139024 |
84,037 | cia_declassified | -1 | -0.877925 | 9.18998 |
84,041 | cia_declassified | -1 | -0.847002 | 10.333941 |
84,043 | cia_declassified | -1 | -0.638182 | 10.364724 |
84,047 | cia_declassified | -1 | -0.605474 | 9.048227 |
84,049 | cia_declassified | -1 | -0.646319 | 9.021738 |
84,053 | cia_declassified | 1 | 0.04453 | 11.083469 |
84,055 | cia_declassified | 1 | 0.070005 | 11.125731 |
84,079 | cia_declassified | -1 | -0.317193 | 9.377375 |
84,084 | cia_declassified | 33 | -0.312786 | 9.36437 |
84,087 | cia_declassified | -1 | -0.670738 | 10.245328 |
84,088 | cia_declassified | -1 | -0.721739 | 10.319861 |
84,092 | cia_declassified | 142 | -0.800571 | 8.803557 |
84,093 | cia_declassified | 142 | -0.803621 | 8.78931 |
84,148 | cia_declassified | -1 | -0.500331 | 11.362282 |
84,154 | cia_declassified | -1 | -0.474147 | 11.375797 |
84,161 | cia_declassified | -1 | 5.351289 | -4.243559 |
84,163 | cia_declassified | 0 | 1.616733 | 2.537851 |
84,172 | cia_declassified | -1 | -2.287968 | 11.082216 |
84,173 | cia_declassified | -1 | -2.407084 | 10.791079 |
84,195 | cia_declassified | 0 | 1.678294 | 2.587715 |
84,199 | cia_declassified | 0 | 1.662764 | 2.583273 |
84,224 | cia_declassified | -1 | -1.426197 | 10.474431 |
84,227 | cia_declassified | -1 | -1.219487 | 10.351205 |
84,230 | cia_declassified | 33 | -0.407562 | 9.477496 |
84,231 | cia_declassified | 33 | -0.388051 | 9.440654 |
84,234 | cia_declassified | -1 | -0.352016 | 9.605873 |
84,237 | cia_declassified | -1 | -0.353399 | 9.66025 |
84,520 | cia_declassified | -1 | -1.021537 | 10.129214 |
84,625 | cia_declassified | -1 | -0.827467 | 9.582291 |
84,629 | cia_declassified | -1 | -0.76925 | 9.463367 |
84,631 | cia_declassified | -1 | -0.780621 | 9.504166 |
84,634 | cia_declassified | -1 | -0.741051 | 9.43696 |
84,643 | cia_declassified | -1 | -0.453408 | 9.341114 |
84,645 | cia_declassified | -1 | -0.471909 | 9.30743 |
84,656 | cia_declassified | -1 | -0.559729 | 9.22828 |
84,658 | cia_declassified | -1 | -0.533875 | 9.239689 |
84,663 | cia_declassified | -1 | -0.736715 | 9.139273 |
84,667 | cia_declassified | -1 | -0.763929 | 9.155208 |
84,673 | cia_declassified | 2 | -0.864548 | 8.993828 |
84,675 | cia_declassified | 2 | -0.861977 | 8.981841 |
84,690 | cia_declassified | 33 | -0.389465 | 9.295588 |
84,692 | cia_declassified | 33 | -0.385971 | 9.293338 |
84,702 | cia_declassified | -1 | -0.573589 | 9.199207 |
84,703 | cia_declassified | -1 | -0.557409 | 9.204577 |
84,765 | cia_declassified | -1 | -0.537898 | 9.252768 |
84,769 | cia_declassified | -1 | -0.52183 | 9.265844 |
84,803 | cia_declassified | -1 | -0.823566 | 9.151765 |
84,822 | cia_declassified | 2 | -0.809868 | 9.089261 |
84,828 | cia_declassified | -1 | -0.497485 | 9.278318 |
84,830 | cia_declassified | 33 | -0.493857 | 9.260844 |
84,834 | cia_declassified | -1 | -0.786067 | 9.089924 |
84,836 | cia_declassified | 2 | -0.783116 | 9.069794 |
84,879 | cia_declassified | -1 | -0.547243 | 9.271632 |
84,882 | cia_declassified | 33 | -0.504314 | 9.327998 |
84,934 | cia_declassified | -1 | -0.158679 | 10.738867 |
84,939 | cia_declassified | -1 | -0.187291 | 10.730396 |
84,942 | cia_declassified | -1 | -0.763351 | 8.892154 |
84,943 | cia_declassified | -1 | -0.78042 | 8.875224 |
84,945 | cia_declassified | -1 | -0.757706 | 8.901073 |
84,947 | cia_declassified | -1 | -0.773473 | 8.877249 |
84,949 | cia_declassified | -1 | -0.774171 | 8.933896 |
84,950 | cia_declassified | -1 | -0.775785 | 8.96982 |
84,952 | cia_declassified | -1 | -0.787338 | 8.91762 |
84,954 | cia_declassified | -1 | -0.738008 | 8.860472 |
84,975 | cia_declassified | -1 | -0.188948 | 10.922667 |
84,978 | cia_declassified | -1 | -0.889586 | 10.441299 |
84,980 | cia_declassified | -1 | -0.310296 | 9.755919 |
End of preview.
YAML Metadata Warning:The task_categories "text-mining" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Research Document Archive
234,630 declassified U.S. government documents processed through a 13-step ML pipeline. 3.2 million pages OCR'd, 31 million named entities extracted and linked, 288 topic clusters identified.
Live platform: tanglewoodapp.com
Collections
| Collection | Documents | Pages | Size |
|---|---|---|---|
| House Resolutions | 181,092 | 2,719,832 | 34.2 GB |
| JFK Assassination Records | 35,979 | 241,860 | 22.5 GB |
| CIA Stargate Program | 13,937 | 100,056 | 5.4 GB |
| CIA MKUltra | 1,936 | 64,244 | 3.4 GB |
| CIA Declassified | 1,605 | 29,744 | 2.4 GB |
| Lincoln Archives | 21 | 9,330 | 962.9 MB |
ML Pipeline (13 Steps)
- Document ingestion and format normalization
- OCR with Tesseract + post-correction
- Classification stamp detection (SECRET, CONFIDENTIAL, UNCLASSIFIED, etc.)
- Redaction detection and boundary mapping
- Named entity recognition (people, organizations, locations, dates)
- Entity disambiguation and cross-document linking
- Relationship extraction
- Topic modeling (LDA + BERTopic)
- Timeline event extraction
- Network graph construction
- Sentiment and tone analysis
- Document similarity clustering
- Index building for search and retrieval
Classification Stamps Detected
| Stamp | Count |
|---|---|
| UNCLASSIFIED | 16,501 |
| SECRET | 13,736 |
| CLASSIFIED | 10,730 |
| EXEMPT | 6,739 |
| CONFIDENTIAL | 5,554 |
| RESTRICTED | 4,722 |
Key Statistics
- 31M named entities extracted
- 2.9M entity cross-document links
- 59,830 redactions detected and mapped
- 288 topic clusters identified
- 6 document collections spanning 1860s–2000s
Usage
from datasets import load_dataset
ds = load_dataset("datamatters24/research-document-archive")
# Filter by collection
jfk = ds.filter(lambda x: x["collection"] == "jfk_assassination")
# Search by entity
cia_docs = ds.filter(lambda x: "CIA" in x["entities"])
Data Sources
All documents are public record obtained from:
- National Archives (NARA)
- CIA FOIA Reading Room
- Congress.gov
- Library of Congress
Citation
@misc{rubin2026researcharchive,
author = {Rubin, Theodore},
title = {Research Document Archive: ML Pipeline for Declassified U.S. Government Documents},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/datamatters24/research-document-archive}
}
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