Datasets:

joelniklaus
/

mc4_legal

index int32 48 41.6M	url stringlengths 14 3.34k	timestamp timestamp[s]	matches sequence	text stringlengths 130 190k
64,082	http://eulaw.egov.bg/DocumentsList.aspx?idstr=27188	2018-08-18T02:45:11	[ "ДЕЛО 48", "ДЕЛО 9", "ДЕЛО 25", "ДЕЛО 44", "ДЕЛО 7", "ДЕЛО 5", "ДЕЛО 8", "ДЕЛО 13", "ДЕЛО 294" ]	Съд на Европейските общности ( 0 структури и 678 документа ) (преюдициално запитване от Consiglio di Stato — Италия) — Consorzio Elisoccorso San Raffaele/Elilombarda s.r.l., Azienda Ospedaliera Ospedale Niguarda Ca'Granda di Milano (Дело C-492/06) (1) (Обществени поръчки — Директива 89/665/ЕИО — Производство по ... ALG...
74,442	https://rs-teteven.org/wp-content/uploads/2010/WEB_VALID.html	2019-08-18T15:11:10	["дело 624","дело 139","дело 673","дело 366","дело 312","дело 445","дело(...TRUNCATED)	"за периода от 1.1.2010г. до 31.12.2010г.\n1 Гражданско дело No 26/2003(...TRUNCATED)
87,298	http://sv-news.blogspot.com/2016/	2019-03-18T14:41:56	["дело 103","дело 103","дело 103","ДЕЛО 2192","дело 1885","дело 91","дел(...TRUNCATED)	"Spartak-Varna.net [ Novini+ ]: 2016\nпетък, декември 23, 2016\nИлко Станчев(...TRUNCATED)
201,275	http://admcourt-bs.org/CMS_ADM/images_content/505_2018O.htm	2018-10-18T17:41:39	[ "дело 505", "дело 4261", "Дело 739", "дело 2709", "дело 4261", "Дело 739" ]	"Определение по Административно дело 505/2018г.\n26.02.2018 г., г(...TRUNCATED)
288,356	http://appealcourt-varna.org/cp/upload/g-530-2015.htm	2019-05-20T22:47:17	["дело 20835","дело 20835","дело 20835","дело 20835","дело 20835","дело 900"(...TRUNCATED)	"16.12.2015 г., гр. Варна\nАпелативен съд – Варна, Гражданско (...TRUNCATED)
350,075	http://troyan.court-bg.org/img/File/2016/IVANOVA/GD853.14.OBEZ.htm	2018-01-16T23:40:25	["дело 506","дело 573","дело 506","дело 506","дело 65","дело 971","дело (...TRUNCATED)	"Гр.Троян, 22.03.2016 година\nТроянски районен съд, втори гра(...TRUNCATED)
443,871	"https://activeconsult.net/%D1%83%D0%BA%D1%80%D0%B8%D0%B2%D0%B0%D0%BD%D0%B5-%D0%BD%D0%B0-%D0%BF%D1%8(...TRUNCATED)	2019-10-15T23:52:05	[ "de lege ferenda", "дело 19", "дело 6435", "дело 14876", "дело 11423", "дело 6931" ]	"Укриване на приходи и доходи като основание за ревизи(...TRUNCATED)
502,238	http://paragraf12.com/normativ/_8_15_1_09_g_vyrhoven_kasacionen_syd_sofiq/22834	2013-12-10T18:38:16	["дело 787","дело 680","дело 5087","дело 210","дело 22","дело 332","дело(...TRUNCATED)	"№ 8/15.1.09 г. - Върховен касационен съд София\nАктовеДекла(...TRUNCATED)
613,280	http://www.constcourt.bg/bg/Acts/GetHtmlContent/ec2db053-a630-45a3-a776-0acca79e5aa8	2018-08-18T17:40:46	["дело 122","дело 74","дело 98","дело 112","дело 230","дело 84","дело 52(...TRUNCATED)	"конституционно дело № 12/ 2016 г.\nсъдия- докладчик Мариан(...TRUNCATED)
723,152	"https://meta.wikimedia.org/wiki/Creative_Commons_BG_4.0?fbclid=IwAR2j_vBRDY-pQzkUzPHakoSOxqoFGnuiJh(...TRUNCATED)	2020-07-03T11:19:06	["Sui Generis","Sui Generis","Sui Generis","Sui Generis","Sui Generis","Sui Generis","Sui Generis","(...TRUNCATED)	"Creative Commons BG 4.0 - Meta\nCreative Commons BG 4.0\nFrom https://wiki.creativecommons.org/wiki(...TRUNCATED)

End of preview. Expand in Data Studio

Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages

Dataset Summary

This dataset contains large text resources (~133GB in total) from mc4 filtered for legal data that can be used for pretraining language models.

Use the dataset like this:

from datasets import load_dataset
dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True)

Supported Tasks and Leaderboards

The dataset supports the task of masked language modeling.

Languages

The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv

Dataset Structure

Data Instances

The file format is jsonl.xz and there is one split available ("train").

Source	Size (MB)	Words	Documents	Words/Document
all	448980	28599300521	9873288	2896
bg	57	2390349	379	6306
cs	31005	1840827375	677796	2715
da	162	10466716	3231	3239
de	105739	6184578784	3164461	1954
el	30	1155977	307	3765
en	13734	966539309	359283	2690
es	132053	9058939804	2281888	3969
et	2059	110198368	49987	2204
fi	1270	62799074	44875	1399
fr	30878	2117306229	598983	3534
ga	1	32772	8	4096
hu	4677	244911748	58857	4161
it	46957	3053920779	990823	3082
lt	156	9142223	1529	5979
lv	1	58702	16	3668
mt	65	3479869	731	4760
nl	326	21962633	6875	3194
pl	37950	2235839721	827641	2701
pt	20120	1338147828	382173	3501
ro	8816	551372510	136513	4038
sk	5850	349265172	130701	2672
sl	1742	107493024	32574	3299
sv	5332	328471555	123657	2656

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

The dataset was created by filtering mc4 for legal data. We used terms indicating legal citations to get the texts. Note that this dataset can be quite noisy, and the quality is not known.