OpenCulture
Collection
A multilingual dataset of public domain books and newspapers.
•
27 items
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Updated
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117
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
identifier: string
creator: string
title: string
publication_date: int32
language: string
language_code: string
text: string
word_count: int32
character_count: int32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1142
to
{'identifier': Value(dtype='string', id=None), 'creator': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'publication_date': Value(dtype='int64', id=None), 'word_count': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1492, in compute_config_parquet_and_info_response
fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 683, in fill_builder_info
) = retry_validate_get_features_num_examples_size_and_compression_ratio(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 602, in retry_validate_get_features_num_examples_size_and_compression_ratio
validate(pf)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 640, in validate
raise TooBigRowGroupsError(
worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 1171859236 which exceeds the limit of 300000000
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
for item in generator(*args, **kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 97, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 75, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
identifier: string
creator: string
title: string
publication_date: int32
language: string
language_code: string
text: string
word_count: int32
character_count: int32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1142
to
{'identifier': Value(dtype='string', id=None), 'creator': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'publication_date': Value(dtype='int64', id=None), 'word_count': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
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 1505, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, 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.
identifier
string | creator
string | title
string | publication_date
int64 | word_count
int64 | text
string |
|---|---|---|---|---|---|
narodni_koledar_1863-j_sundecic | J. Sundečić | Narodni koledar novi i stari za prostu godinu 1863 | 1,863 | 34,054 | "\n\n\n\n\n\nOsterreichische Nationalbibliothek \n\n\n +£254028206 \n\n\n€ 7 \n\n\nNARODNI \n\n\n(...TRUNCATED) |
narodnesrpskepj01karagoog | Vuk Stefanović Karadžić | Narodne srpske pjesme | 1,833 | 66,701 | "Google \n\n\n\nThis is a digital copy of a book that was preserved for generations on library shelv(...TRUNCATED) |
uputa_u_pjesmenu_umjetnost_1852-emanuel_sladovic | Emanuel Sladović | Uputa u pjesmenu umjetnost (1852) | 1,852 | 16,357 | "This is a reproduction of a library book that was digitized \nby Google as part of an ongoing effor(...TRUNCATED) |
pucki_prijatelj_1874_tecaj_osmi-dragutin_jagic | Dragutin Jagić | Pučki prijatelj. Poučan i zabavan časopis za puk trojedne kraljevine. Tečaj osmi. 1874. | 1,874 | 152,170 | "This is a reproduction of a library book that was digitized \nby Google as part of an ongoing effor(...TRUNCATED) |
ivan_kukuljevic_sakcinski_1861-stjepan_mirkovic | Stjepan Mirković | Ivan Kukuljević Sakcinski | 1,861 | 26,563 | "This is a reproduction of a library book that was digitized \nby Google as part of an ongoing effor(...TRUNCATED) |
pokusna_fizika_za_pucke_ucitelje_1869 | null | Pokusna fizika za pučke učitelje | 1,869 | 25,459 | "x “Oj \nk > # \n\n\nS. _ \n\n\nWIEN \n\n\nE \n\n\nIONALBIBLHUTHEK \n\n\nSNA \n\n\nDigitized by (G(...TRUNCATED) |
glasniksrpskoga46drugoog | Srpsko učeno društvo , Srpsko učeno društvo | Glasnik Srpskoga učenog društva ...: građ za noviju srpsku istoriju | 1,862 | 73,504 | "Google \n\n\n\nThis is a digital copy of a book that was preserved for generations on library shelv(...TRUNCATED) |
pjesnici_hrvatski_xvi_vieka_razdjel_prvi_1858-ivan_kukuljevic_sakcinski | Ivan Kukuljević Sakcinski | Pjesnici hrvatski XVI. vieka razdjel prvi | 1,858 | 37,656 | "PJESNICI HRVATSKI \n\n\nXVI. VIBKA \n\n\noD \n\n\nIVANA KUKULJEVIĆA SAKCINSKOGA \n\n\nRAZDJEL PRVI(...TRUNCATED) |
stari_pisci_hrvatski_knjiga_01-jazu_1869 | Jugoslavenska akademija znanosti i umjetnosti | Stari pisci hrvatski. Knjiga prva. 1869. Pjesme Marka Marulića | 1,869 | 122,037 | "Google \n\n\nThis is a digital copy of a book that was preserved for generations on library shelves(...TRUNCATED) |
srpskabibliogra00novagoog | Novaković, Stojan 1842-1915 | Srpska biblijografija za noviju književnost 1741-1867 | 1,869 | 158,726 | "Соодје \n\n\nТа 15 а фема! сору оГа Боок (ћаг аз ргезегуса ((...TRUNCATED) |
End of preview.
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🇷🇸 Serbian Public Domain 🇷🇸
Serbian-Public Domain or Serbian-PD is a large collection aiming to aggregate all Serbian monographies and periodicals in the public domain. As of March 2024, it is the biggest Serbian open corpus.
Dataset summary
The collection contains 1,405 titles making up 156,712,807 words recovered from multiple sources, including Internet Archive and various European national libraries and cultural heritage institutions. Each parquet file has the full text of 2,000 books selected at random.
Curation method
The composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years. Additionally, the initial consolidation of public domain status for cultural heritage operates in the EU under the 2019 Copyright Directive (art. 14).
As of March 2024, to limit rights verification, we have retained exclusively titles published prior to 1884.
The corpus will be expanded at a later stage to encompass late 19th century and early 20th century publications, after checking for public domain validity.
Uses
The collection aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.
The rationales for creation of this collection are multifold:
- Scientific: We observe that the closure of training corpora represents a major barrier to AI research. Large language models face a real crisis of reproducibility.
- Legal: With the adoption of the AI Act with its obligations in terms of copyright law compliance for the pretraining corpora, the European AI ecosystem will have to change its provenance practices.
- Cultural: The linguistic diversity of the European Union is currently underrepresented. Unlike web archives, open, heritage, administrative, or scientific texts are often of high quality: they are long, multilingual, and editorialized publications.
- Economical: Today, value capture is concentrated on players whose financial resources are already considerable, allowing them to collect or purchase data at a high price. Making a royalty-free corpus available to as many people as possible frees innovation in uses and minimizes economic dependencies on dominant actors.
License
The entire collection is in the public domain in all regions. This means that the patrimonial rights of each individual or collective right holders have expired.
There has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that "Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation." (art. 14)
Future work
This dataset is not a one-time work but will continue to evolve significantly in three directions:
- Expansion of the dataset to the late 19th and early 20th century works and its further enhancement with currently unexploited collections coming from European patrimonial data repositories.
- Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.
- Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well-formatted.
Acknowledgements
The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC).
Corpus collection has been largely facilitated thanks to the open science LLM community insights, cooperation and support (Occiglot, Eleuther AI, OpenLLM France, Allen AI).
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