Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found halvest.py
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 989, in dataset_module_factory
                  raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
              RuntimeError: Dataset scripts are no longer supported, but found halvest.py

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HALvest

Open Scientific Papers Harvested from HAL (Unfiltered)

Dataset Summary

overview:

This is the unfiltered version of HALvest, comprising of fulltext from open papers found on Hyper Articles en Ligne (HAL) with extra fields for potential filtering. Our dump is mostly english/french but gather papers written in 56 languages across 13 domains.

You can download the dataset using Hugging Face datasets:

from datasets import load_dataset

ds = load_dataset("almanach/HALvest", "en")

Details

Building the dataset is a three steps process: data fetching from HAL, data merging and data enriching.

We first request HAL's API in order to gather open research papers and parse it -- effectively sorting papers by language. Then, we download the PDFs of the fetched data.
Using GROBID, we convert each PDF to an xml-tei format in order to have structured data. We convert each xml-tei file to a txt format before concatenating it with the paper's.
Finally, we compute some statistics about each document.

Languages

Please, note that the number of tokens is highly inflated in the raw version of the dataset because of badly encoded PDFs, translating to gibberish documents/texts.

ISO-639	Language	# Documents	# mT5 Tokens
en	English	464,679	8,158,933,235
fr	French	199,216	9,018,529,985
es	Spanish	2,975	69,221,667
it	Italian	1,172	48,747,986
pt	Portuguese	934	32,918,832
de	German	652	12,225,960
ru	Russian	245	5,763,532
zh	Chinese	160	2,861,585
eu	Basque	113	2,297,485
ar	Arabic	92	2,167,431
ja	Japanese	92	547,861
el	Greek	54	1,738,878
pl	Polish	43	987,878
ro	Romanian	39	1,298,901
uk	Ukrainian	34	837,793
vi	Viêt Namese	29	436,660
ca	Catalan	28	975,078
da	Danish	27	961,955
oc	Occitan	26	285,334
br	Breton	24	998,088
sr	Serbian	24	336,878
ko	Korean	17	226,268
fa	Persian	17	213,903
tr	Turkish	17	149,718
hu	Hungarian	14	577,568
eo	Esperanto	14	105,286
hy	Armenian	10	127,988
cs	Czech	9	712,263
bg	Bulgarian	9	208,763
sq	Albanian	9	98,009
id	Indonesian	9	53,075
he	Hebrew	8	61,283
hr	Croatian	8	40,621
et	Estonian	7	20,405
sv	Swedish	6	270,642
no	Norwegian	6	62,767
az	Azerbaijani	5	52,762
fi	Finnish	4	60,507
tet	Tetum	4	18,485
lt	Lithuanian	3	16,572
mr	Marathi	3	16,386
hi	Hindi	3	3,490
ie	Interlingue	2	140,383
ta	Tamil	2	77,087
sw	Swahili	2	73,921
tl	Tagalog	2	35,962
gl	Galician	2	29,688
mk	Macedonian	2	14,654
th	Thai	1	70,909
tk	Turkmen	1	66,104
bs	Bosnian	1	63,018
kk	Kazakh	1	41,839
sl	Slovenian	1	22,844
sk	Slovak	1	12,997
co	Corsican	1	9,083
gn	Guarani	1	1,566
bo	Tibetan	1	579

Domains

Please, note that the number of tokens is highly inflated in the raw version of the dataset because of badly encoded PDFs, translating to gibberish documents/texts.

Domain	Code	# Documents	# mT5 Tokens
Humanities and Social Sciences	shs	156,566	5,614,423,171
Computer Science	info	148,316	2,573,673,455
Life Sciences	sdv	115,744	3,145,323,780
Engineering Sciences	spi	102,751	2,254,653,825
Physics	phys	65,991	1,503,190,749
Mathematics	math	62,921	1,638,500,361
Chemical Science	chim	40,012	899,507,319
Environmental Science	sde	31,575	579,076,669
Sciences of the Universe	sdu	23,557	682,356,264
Cognitive science	scco	11,772	227,487,096
Statistics	stat	10,579	184,678,350
Quantitative Finance	qfin	3,451	68,518,636
Nonlinear Sciences	nlin	1,972	30,694,088

You can browse through every domains and sub-domains here: https://hal.science/browse/domain.

Considerations for Using the Data

The corpus is extracted from the HAL's open archive which distributes scientific publications following open access principles. The corpus is made up of both creative commons licensed and copyrighted documents (distribution authorized on HAL by the publisher). This must be considered prior to using this dataset for any purpose, other than training deep learning models, data mining etc. We do not own any of the text from which these data has been extracted.

Citation

@misc{kulumba2024harvestingtextualstructureddata,
      title={Harvesting Textual and Structured Data from the HAL Publication Repository}, 
      author={Francis Kulumba and Wissam Antoun and Guillaume Vimont and Laurent Romary},
      year={2024},
      eprint={2407.20595},
      archivePrefix={arXiv},
      primaryClass={cs.DL},
      url={https://arxiv.org/abs/2407.20595}, 
}

Dataset Copyright

The licence terms for HALvest strictly follows the one from HAL. Please refer to the below license when using this dataset.

HAL license

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Repository:

GitHub

Models trained or fine-tuned on almanach/halvest