nlpso/m2m3_fine_tuning_ocr_cmbert_io
收藏Hugging Face2023-02-22 更新2024-03-04 收录
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https://hf-mirror.com/datasets/nlpso/m2m3_fine_tuning_ocr_cmbert_io
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资源简介：
---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---
# m2m3_fine_tuning_ocr_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **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 : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
## 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_fine_tuning_ocr_cmbert_io")
提供机构：
nlpso
原始信息汇总
m2m3_fine_tuning_ocr_cmbert_io 数据集概述
简介
该数据集用于微调 Jean-Baptiste/camembert-ner 模型，针对嵌套命名实体识别任务使用独立命名实体识别层方法 [M1]。数据包含19世纪巴黎的商业目录条目。
数据集参数
- 方法：M2 和 M3
- 数据类型：噪声数据（Pero OCR）
- 分词器：Jean-Baptiste/camembert-ner
- 标记格式：IO
- 数据量：
- 训练集：6084
- 开发集：676
- 测试集：1685
- 关联的微调模型：
实体类型
| 缩写 | 实体组（级别） | 描述 |
|---|---|---|
| O | 1 & 2 | 非命名实体 |
| PER | 1 | 人名或公司名 |
| ACT | 1 & 2 | 人或公司的职业活动 |
| TITREH | 2 | 军事或民事区分 |
| DESC | 1 | 条目完整描述 |
| TITREP | 2 | 职业奖励 |
| SPAT | 1 | 地址 |
| LOC | 2 | 街道名称 |
| CARDINAL | 2 | 街道号码 |
| FT | 2 | 地理特征 |
如何使用该数据集
python from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_io")
