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Datasets:
nlpso
/
m1_fine_tuning_ref_cmbert_iob2

Tasks:
Token Classification
Modalities:
Text
Formats:
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Languages:
French
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1K - 10K
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tokens
sequencelengths
4
124
ner_tags_niv1
sequencelengths
4
124
ner_tags_niv2
sequencelengths
4
124
input_ids
sequencelengths
8
201
attention_mask
sequencelengths
8
201
labels_niv1
sequencelengths
8
201
labels_niv2
sequencelengths
8
201
[ "Maret", ",", "entrepr.", "de", "macon", ".", ",", "Grand-St-Michel", ",", "20", "." ]
[ "B-PER", "O", "B-ACT", "I-ACT", "I-ACT", "I-ACT", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "O", "O", "O", "O", "O", "B-LOC", "O", "B-CARDINAL", "O" ]
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[ "JAMET", "père", "et", "fils", ",", "r.", "des", "Fossés-St.-Bernard", ",", "59", "." ]
[ "B-PER", "I-PER", "I-PER", "I-PER", "O", "B-SPAT", "I-SPAT", "I-SPAT", "I-SPAT", "I-SPAT", "O" ]
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[ "Mignardet", ",", "horloger", ",", "r.", "de", "Grenelle-S.-", "Honoré", ",", "3o", ".", "376" ]
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[ "Lemarchand", ",", "dépôt", "de", "velours", "d'Utrecht", "et", "moquettes", ",", "r.", "du", "Pet.-Carreau", ",", "3", "." ]
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[ "Colon", "(", "PP", ".", ")", ",", "méd", ".", ",", "Godot", ",", "34", "." ]
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[ "Germain", ",", "pâtissier", ",", "Arcole", ",", "19", "." ]
[ "B-PER", "O", "B-ACT", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
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[ "Doizy", ",", "fab", "d'anches", "de", "clarinettes", ",", "Marais-", "St-Martin", ",", "25", "." ]
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[ "Grandeffe", "(", "Comte", "de", ")", ",", "cité", "d'Antin", ",", "8", "." ]
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[ "Digues", ",", "nouveautés", ",", "St-Denis", ",", "116", "," ]
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[ "Milerin", ",", "march.", "de", "vins", ",", "r.", "Croix-des-Petits-", "Champs", ",", "13", ".", "502" ]
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[ "Duffau-Pauillac", "(", "Chs", ")", ",", "Enghien", ",", "16", ".", "*" ]
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[ "Gard", "Letertre", "(", "Mlle.J", ".", ")", ",", "fab", ".", ",", "r.", "Ste.-Anne", ",", "59", "." ]
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[ "Borel", "dit", "Lamy", "(", "Mme", ")", ",", "tabac", ",", "Montmartre", ",", "81", "." ]
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[ "Delatourmignière", ",", "greff", ".", "1re", "inst", ".", ",", "boul.", "St-Martin", ",", "4", "." ]
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[ "Vauvray", "jeune", ",", "ferblantier-lam-", "piste", ",", "Neuve-St-Etienne-Bonne-", "Nouvelle", ",", "16", "." ]
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[ "Doine", ",", "distill", ".", ",", "Montagne-Ste-Genevieve", ".", "24", "." ]
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[ "Alessandri", ",", "tabletier", ",", "Folie-Méricourt", ",", "21", ".", "|" ]
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[ -100, 0, -100, -100, 0, 0, -100, -100, 0, -100, 18, -100, 8, -100, -100, -100, -100, 0, -100, 19, 0, -100, 0, -100, 0, 0, 0, -100, 0, 0, 0, 0, 0, 0, -100, 0, 0, 0, 0, -100, 0, 0, 0, 0, 0, 0, -100, -100 ]
[ "Badenier", ",", "élig", ".", ",", "archit", ".", ",", "Meslay", ",", "40", "." ]
[ "B-PER", "I-PER", "I-PER", "I-PER", "O", "B-ACT", "I-ACT", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "B-TITREH", "I-TITREH", "O", "O", "O", "O", "B-LOC", "O", "B-CARDINAL", "O" ]
[ 5, 8741, 35, 3738, 21, 7, 1619, 8027, 21, 9, 21, 7, 15768, 110, 21, 9, 21, 7, 2658, 3965, 21, 7, 962, 21, 9, 6 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ -100, 11, -100, -100, 1, -100, 1, -100, 1, -100, 0, -100, 17, -100, 7, -100, 0, -100, 14, -100, 4, -100, 4, 0, -100, -100 ]
[ -100, 0, -100, -100, 0, -100, 15, -100, 5, -100, 0, -100, 0, -100, 0, -100, 0, -100, 18, -100, 0, -100, 19, 0, -100, -100 ]
[ "Lacombe", ",", "maître", "maçon", ",", "Ste-Marguerite-St-Germ", ".", ",", "40", "." ]
[ "B-PER", "O", "B-ACT", "I-ACT", "O", "B-SPAT", "I-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "O", "O", "O", "B-LOC", "I-LOC", "O", "B-CARDINAL", "O" ]
[ 5, 61, 21182, 21, 7, 2300, 23634, 21, 7, 8237, 26, 465, 16873, 5727, 26, 7713, 26, 546, 108, 215, 21, 9, 21, 7, 962, 21, 9, 6 ]
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[ -100, 11, -100, 0, -100, 17, 7, 0, -100, 14, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 4, -100, 4, -100, 4, 0, -100, -100 ]
[ -100, 0, -100, 0, -100, 0, 0, 0, -100, 18, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 8, -100, 0, -100, 19, 0, -100, -100 ]
[ "Baillon", ",", "pharmacien", ",", "Cadet", ",", "19", "." ]
[ "B-PER", "O", "B-ACT", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "O", "O", "B-LOC", "O", "B-CARDINAL", "O" ]
[ 5, 2610, 3394, 21, 7, 13341, 21, 7, 13199, 231, 21, 7, 653, 21, 9, 6 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ -100, 11, -100, 0, -100, 17, 0, -100, 14, -100, 4, -100, 4, 0, -100, -100 ]
[ -100, 0, -100, 0, -100, 0, 0, -100, 18, -100, 0, -100, 19, 0, -100, -100 ]
[ "Alès", "(", "Vicomtesse", "d", "'", ")", ",", "Caumartin", ",", "73", "." ]
[ "B-PER", "I-PER", "I-PER", "I-PER", "I-PER", "I-PER", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "O", "O", "O", "O", "O", "B-LOC", "O", "B-CARDINAL", "O" ]
[ 5, 114, 10243, 38, 2462, 310, 110, 3609, 18, 21, 11, 936, 21, 7, 84, 276, 24617, 21, 7, 10335, 21, 9, 6 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ -100, 11, -100, 1, 1, -100, -100, -100, 1, 1, -100, 1, 0, -100, 14, -100, -100, 4, -100, 4, 0, -100, -100 ]
[ -100, 0, -100, 0, 0, -100, -100, -100, 0, 0, -100, 0, 0, -100, 18, -100, -100, 0, -100, 19, 0, -100, -100 ]
[ "Parain", ",", "Petit-Lion", ",", "38", "." ]
[ "B-PER", "O", "B-SPAT", "I-SPAT", "I-SPAT", "O" ]
[ "O", "O", "B-LOC", "O", "B-CARDINAL", "O" ]
[ 5, 372, 4526, 21, 7, 2233, 26, 370, 1412, 21, 7, 3294, 21, 9, 6 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ -100, 11, -100, 0, -100, 14, -100, -100, -100, 4, -100, 4, 0, -100, -100 ]
[ -100, 0, -100, 0, -100, 18, -100, -100, -100, 0, -100, 19, 0, -100, -100 ]
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m1_fine_tuning_ref_cmbert_iob2

Introduction

This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century.

Dataset parameters

Entity types

Abbreviation Entity group (level) Description
O 1 & 2 Outside of a named entity
PER 1 Person or company name
ACT 1 & 2 Person or company professional activity
TITREH 2 Military or civil distinction
DESC 1 Entry full description
TITREP 2 Professionnal reward
SPAT 1 Address
LOC 2 Street name
CARDINAL 2 Street number
FT 2 Geographical feature

How to use this dataset 

from datasets import load_dataset

train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_cmbert_iob2")
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