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---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags_niv1
sequence: string
- name: ner_tags_niv2
sequence: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels_niv1
sequence: int64
- name: labels_niv2
sequence: int64
splits:
- name: train
num_bytes: 5034023
num_examples: 6084
- name: dev
num_bytes: 552733
num_examples: 676
- name: test
num_bytes: 1343029
num_examples: 1685
download_size: 1000353
dataset_size: 6929785
---
# m1_fine_tuning_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **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") |