
# m1_fine_tuning_ref_ptrn_cmbert_io

## Introduction

This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/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.

## Dataset parameters

* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts : 
    * Train : 6084
    * Dev : 676
    * Test : 1685
* Associated fine-tuned models :
    * Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1)
    * Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_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_ptrn_cmbert_io")

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 

m1\_fine\_tuning\_ref\_ptrn\_cmbert\_io
=======================================


Introduction
------------


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.


Dataset parameters
------------------


* Approach : M1
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
	+ Train : 6084
	+ Dev : 676
	+ Test : 1685
* Associated fine-tuned models :
	+ Level-1 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_1
	+ Level 2 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_2


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/m1\_fine\_tuning\_ref\_ptrn\_cmbert\_io")



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

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