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m2m3_qualitative_analysis_ocr_cmbert_io
Introduction
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century.
Dataset parameters
- Approachrd : M2 and M3
- Dataset type : noisy (Pero OCR)
- Tokenizer : Jean-Baptiste/camembert-ner
- Tagging format : IO
- Counts :
- Train : 6084
- Dev : 676
- Test : 1685
- Associated fine-tuned models :
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/m2m3_qualitative_analysis_ocr_cmbert_io")
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