Datasetpublic

nlpso/m1_fine_tuning_ref_cmbert_io

m1_fine_tuning_ref_cmbert_io 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 Approach : M1 Dataset type : ground-truth Tokenizer : Jean-Baptiste/camembert-ner Tagging format : IO Counts : Train : 6084 Dev : 676 Test : 1685 Associated fine-tuned models : Level-1 :… See the full description on the dataset page: https://huggingface.co/datasets/nlpso/m1_fine_tuning_ref_cmbert_io.

sourceHugging Faceupdated 4y agoView on Hugging Face

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Dataset Card

m1finetuningrefcmbert_io

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

—Approach : M1
—Dataset type : ground-truth
—Tokenizer : Jean-Baptiste/camembert-ner
—Tagging format : IO
—Counts :
—Train : 6084
—Dev : 676
—Test : 1685
—Associated fine-tuned models :
—Level-1 : nlpso/m1_ind_layers_ref_cmbert_io_level_1
—Level 2 : nlpso/m1_ind_layers_ref_cmbert_io_level_2

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

python

from datasets import load_dataset

train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_cmbert_io")