nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io

---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---

# m1_fine_tuning_ocr_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 : noisy (Pero OCR)
* 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_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_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_ocr_ptrn_cmbert_io")

原始信息汇总

数据集概述

基本信息

• 数据集名称：m1_fine_tuning_ocr_ptrn_cmbert_io
• 语言：法语
• 多语言性：单语
• 任务类别：token-classification

数据集描述

• 用途：用于微调模型HueyNemud/das22-10-camembert_pretrained，以支持嵌套命名实体识别任务。
• 数据来源：包含19世纪巴黎贸易目录条目。

数据集参数

• 方法：M1
• 数据集类型：噪声数据（Pero OCR）
• 分词器：HueyNemud/das22-10-camembert_pretrained
• 标记格式：IO
• 数据集大小：
  • 训练集：6084
  • 验证集：676
  • 测试集：1685
• 关联的微调模型：
  • 一级模型：nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1
  • 二级模型：nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2

实体类型

缩写	实体组(级别)	描述
O	1 & 2	非实体部分
PER	1	人名或公司名
ACT	1 & 2	人或公司的职业活动
TITREH	2	军事或民事区别
DESC	1	条目全描述
TITREP	2	职业奖励
SPAT	1	地址
LOC	2	街道名
CARDINAL	2	街道号码
FT	2	地理特征

使用方法

python from datasets import load_dataset

train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io")