nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1
Token Classification
•
Updated
•
6
tokens
sequencelengths 4
125
| ner_tags_niv1
sequencelengths 4
125
| ner_tags_niv2
sequencelengths 4
125
| input_ids
sequencelengths 8
203
| attention_mask
sequencelengths 8
203
| labels_niv1
sequencelengths 8
203
| labels_niv2
sequencelengths 8
203
|
---|---|---|---|---|---|---|
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[
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"Vie",
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")",
",",
"Bourbon",
",",
"65",
"."
] | [
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"I-PER",
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"O",
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"I-SPAT",
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[
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",",
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"St",
"Martin",
",",
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"."
] | [
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[
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",",
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"et",
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"a",
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"et",
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"I-ACT",
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[
"Badenier",
",",
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".",
",",
"archit",
".",
",",
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",",
"40",
"."
] | [
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"I-PER",
"O",
"I-ACT",
"I-ACT",
"O",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"O"
] | [
"O",
"O",
"I-TITREH",
"I-TITREH",
"O",
"O",
"O",
"O",
"I-LOC",
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1,
1,
1,
1,
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[
"Lacombe",
",",
"maitre",
"maçon",
",",
"Ste-Marguerite-St-Germ",
".",
",",
"40",
"."
] | [
"I-PER",
"O",
"I-ACT",
"I-ACT",
"O",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"O"
] | [
"O",
"O",
"O",
"O",
"O",
"I-LOC",
"I-LOC",
"O",
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"O"
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9,
21,
7,
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21,
9,
6
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] |
[
"Baillon",
",",
"pharmacien",
",",
"Cadet",
",",
"19",
"."
] | [
"I-PER",
"O",
"I-ACT",
"O",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"O"
] | [
"O",
"O",
"O",
"O",
"I-LOC",
"O",
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"O"
] | [
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7,
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21,
7,
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1,
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0,
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] |
[
"Alès",
"(",
"Vicomtesse",
"d'",
")",
",",
"Caumartin",
",",
"73",
"."
] | [
"I-PER",
"I-PER",
"I-PER",
"I-PER",
"I-PER",
"O",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"O"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"I-LOC",
"O",
"I-CARDINAL",
"O"
] | [
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21,
7,
84,
276,
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21,
7,
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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
] | [
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0,
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] |
[
"Parain",
",",
"Petit-Lion",
",",
"38",
"."
] | [
"I-PER",
"O",
"I-SPAT",
"I-SPAT",
"I-SPAT",
"O"
] | [
"O",
"O",
"I-LOC",
"O",
"I-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,
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0,
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-100,
4,
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4,
0,
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-100
] | [
-100,
0,
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8,
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0,
-100,
9,
0,
-100,
-100
] |
This dataset was used to fine-tuned HueyNemud/das22-10-camembert_pretrained for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century.
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 |
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io")