
# m2m3_qualitative_analysis_ocr_cmbert_iob2

## Introduction

This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/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](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts : 
    * Train : 6084
    * Dev : 676
    * Test : 1685
* Associated fine-tuned models :
    * M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2)
    * M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2)
    
## 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/m2m3_qualitative_analysis_ocr_cmbert_iob2")

task_categories:token-classification multilinguality:monolingual language:fr region:us
{"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]}
TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us 

m2m3\_qualitative\_analysis\_ocr\_cmbert\_iob2
==============================================


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 : IOB2
* Counts :
	+ Train : 6084
	+ Dev : 676
	+ Test : 1685
* Associated fine-tuned models :
	+ M2 : nlpso/m2\_joint\_label\_ocr\_cmbert\_iob2
	+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_cmbert\_iob2


Entity types
------------


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


How to use this dataset
-----------------------


'''python
from datasets import load\_dataset


train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ocr\_cmbert\_iob2")



TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us 

passage: TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us 
