stefan-it/co-funer

---
license: mit
task_categories:
- token-classification
language:
- de
---

# CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction

This inofficial dataset repository provides a CoNLL-like version of the CO-Fun **NER** dataset, that was proposed in the CO-Fun paper (https://arxiv.org/abs/2403.15322):

> The process of cyber mapping gives insights in relationships among financial entities and service providers. Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. The labeling process on 948 sentences was carried out by three experts which yields to 5,969 annotations for four entity types (Outsourcing, Company, Location and Software) and 4,102 relation annotations (Outsourcing-Company, Company-Location). State-of-the-art deep learning models were trained to recognize entities and extract relations showing first promising results.

## Preprocessing

The notebook [Export-To-CoNLL.ipynb](Export-To-CoNLL.ipynb) performs the necessary steps to create a CoNLL-like version of the CO-Fun dataset, that could easily be used for fine-tuning NER models.

Additionally, the [FlairDatasetTest.ipynb](FlairDatasetTest.ipynb) notebooks loads the dataset with the Flair dataset loader and checks, if the number of parsed sentences is correct and identical to the number of sentences reported in the official CO-Fun paper.

## Named Entites

The CO-Fun dataset provides annotations for the following Named Entities:

* `Auslagerung` (engl. outsourcing)
* `Unternehmen` (engl. company)
* `Ort` (engl. location)
* `Software`

# Example: Load Dataset with Flair library

The notebooks [FlairDatasetExample.ipynb](FlairDatasetExample.ipynb) shows how to load the dataset with the awesome [Flair library](https://github.com/flairNLP/flair).

# Changelog

* 25.03.2024: Initial version of the preprocessed CO-Fun NER dataset is released.

# Licence

The original CO-Fun dataset is released under MIT license. Thus, this preprocessed version is also licenced under MIT.

---
许可证：MIT协议
任务类别：
- 词元分类（Token Classification）
语言：
- 德语（de）
---

# CO-Fun：面向命名实体识别（Named Entity Recognition，NER）与关系抽取的德国基金招募说明书企业外包数据集

本非官方数据集仓库提供了CO-Fun**命名实体识别（Named Entity Recognition，NER）**数据集的类CoNLL格式版本，该数据集源自CO-Fun相关论文（https://arxiv.org/abs/2403.15322）：

> 网络映射流程可助力挖掘财务实体与服务提供商间的关联关系。本数据集以德国基金招募说明书中企业的外包实践为核心，专为命名实体识别与关系抽取任务打造。948条语句经三位专家完成标注，最终得到针对4类实体（外包、企业、地点与软件）的5969条标注，以及4102条关系标注（外包-企业、企业-地点）。我们已基于前沿深度学习模型开展实体识别与关系抽取训练，初步结果表现极具前景。

## 预处理

配套Jupyter Notebook文件[Export-To-CoNLL.ipynb](Export-To-CoNLL.ipynb)可执行必要步骤，生成适配微调命名实体识别模型的类CoNLL格式CO-Fun数据集。

此外，[FlairDatasetTest.ipynb](FlairDatasetTest.ipynb)可通过Flair数据集加载器读取数据集，并校验解析得到的语句数量是否与官方CO-Fun论文中报告的数量一致。

## 命名实体

CO-Fun数据集为以下命名实体提供标注：
* `Auslagerung`（对应英文outsourcing，即外包）
* `Unternehmen`（对应英文company，即企业）
* `Ort`（对应英文location，即地点）
* `Software`（即软件）

## 示例：基于Flair库加载数据集

配套Notebook文件[FlairDatasetExample.ipynb](FlairDatasetExample.ipynb)演示了如何通过广受好评的[Flair库](https://github.com/flairNLP/flair)加载本数据集。

## 更新记录
* 2024年3月25日：发布预处理后的CO-Fun NER数据集初始版本。

## 授权协议

原始CO-Fun数据集采用MIT协议发布，因此本预处理版本同样遵循MIT授权协议。

原始信息汇总

CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction

Overview

• License: MIT
• Task Categories: Token-classification
• Language: German (de)

Dataset Description

• Purpose: Designed for named entity recognition (NER) and relation extraction tasks.
• Content: Annotated 948 sentences with 5,969 annotations for four entity types (Outsourcing, Company, Location, and Software) and 4,102 relation annotations (Outsourcing-Company, Company-Location).
• Expertise: Labeling process conducted by three experts.
• Results: Trained state-of-the-art deep learning models showing promising results in entity recognition and relation extraction.

Named Entities

• Auslagerung (Outsourcing)
• Unternehmen (Company)
• Ort (Location)
• Software

Preprocessing

• Tools: Export-To-CoNLL.ipynb for creating a CoNLL-like version of the dataset.
• Verification: FlairDatasetTest.ipynb for checking the number of parsed sentences against the official CO-Fun paper.

Usage Example

• Library: Flair
• Example: FlairDatasetExample.ipynb demonstrates loading the dataset using the Flair library.