MaatAI/histoire-general-afrique-global-adaption

---
annotations_creators: []
language:
- fr
- en
language_creators: []
license: apache-2.0
multilinguality:
- monolingual
pretty_name: Svngoku/Histoire-General-Afrique-Global
size_categories:
- 1K<n<10K
source_datasets:
- extended|Svngoku/Histoire-General-Afrique-Global
tags:
- adaption
- instruction-tuning
- history
task_categories:
- question-answering
task_ids: []
---


This dataset is a remastered version of this [dataset](https://huggingface.co/datasets/Svngoku/Histoire-General-Afrique-Global) prepared using [Adaption's](https://adaptionlabs.ai/app/auth) Adaptive Data platform.

# Svngoku/Histoire-General-Afrique-Global

This dataset contains French-language text excerpts detailing the political, social, and economic history of Africa from the 16th to the 18th centuries. The content covers specific regions such as the Lower Guinea Coast and the Zambezi, discussing topics like ethnic migrations, kingdom formations, and trade dynamics. Each sample consists of a single text field derived from historical academic literature.

I started with books from the General History of Africa that had already been split into smaller text chunks.

The initial dataset contained only two fields for each example: the chunk itself and the title of the page it came from.

I used the chunk as the prompt and the page title as context.

Then, I used Adaptive Data to generate the completion and improve the output through hallucination control and alignment steps.

I also added a custom system prompt to better guide how the data was adapted.

### Dataset size

There are 1,315 data points in this dataset. This is an instruction tuning dataset.

### API Code

```py
# Step 1: Create dataset from HuggingFace
dataset = client.datasets.create_from_huggingface(
url="Svngoku/Histoire-General-Afrique-Global",
files=["train.parquet"],
)

# Step 2: Run augmentation
job = client.datasets.run(
dataset.dataset_id,
column_mapping={
"prompt": "content",
"context": ["source"]
},
)
```

### Quality of Remastered Dataset

The final quality is A, with a relative quality improvement of 800.0%.

### Domain
- History (8%)

### Language
- French (100%)

### Tone
- Analytical (16%)
- Informative (8%)
- Historic (4%)

### Evaluation Results

- **Quality Gains:**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/146fcd3c-49e2-40b7-852e-5ec97aa00eaf.png" alt="QualityGains" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

- **Grade Improvement:**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/0e923b0d-e7e0-430c-83c2-97458cfa67e8.png" alt="Grade" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

- **Percentile Chart:**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/030d6eca-efef-40bd-a4a8-27656e6e9326.png" alt="Percentile Chart" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

annotations_creators: 无
language:
- 法语
- 英语
language_creators: 无
license: Apache-2.0
multilinguality:
- 单语
pretty_name: Svngoku/Histoire-General-Afrique-Global
size_categories:
- 1000 < 样本数 < 10000
source_datasets:
- 扩展版|Svngoku/Histoire-General-Afrique-Global
tags:
- 适配（Adaption）
- 指令微调（Instruction Tuning）
- 历史
task_categories:
- 问答（Question Answering）
task_ids: 无
---


本数据集为原[Svngoku/Histoire-General-Afrique-Global数据集](https://huggingface.co/datasets/Svngoku/Histoire-General-Afrique-Global)的重制版，基于[Adaption](https://adaptionlabs.ai/app/auth)的自适应数据平台制作完成。

# Svngoku/Histoire-General-Afrique-Global

本数据集包含法语文本节选，详细阐述了16至18世纪非洲的政治、社会与经济发展历程。内容涵盖下几内亚海岸、赞比西河等特定区域，探讨了族群迁徙、王国建立与贸易动态等议题。每条样本均包含一个源自历史学术文献的单一文本字段。

本次制作的初始素材取自《非洲通史》中已被拆分为更小文本片段的内容。

初始数据集的每条样本仅包含两个字段：文本片段本身，以及其来源页面的标题。

我将该文本片段用作提示词（Prompt），并以页面标题作为上下文（Context）。

随后，我借助Adaptive Data平台生成补全内容，并通过幻觉控制与对齐步骤优化输出结果。

此外，我还添加了自定义系统提示词，以更好地指导数据集的适配流程。

### 数据集规模

本数据集共包含1315条数据样本，属于指令微调数据集。

### 接口代码

py
# Step 1: Create dataset from HuggingFace
dataset = client.datasets.create_from_huggingface(
url="Svngoku/Histoire-General-Afrique-Global",
files=["train.parquet"],
)

# Step 2: Run augmentation
job = client.datasets.run(
dataset.dataset_id,
column_mapping={
"prompt": "content",
"context": ["source"]
},
)

### 重制数据集质量

最终质量评级为A级，相对质量提升幅度达800.0%。

### 领域
- 历史（8%）

### 语言
- 法语（100%）

### 语气
- 分析性（16%）
- 信息性（8%）
- 历史性（4%）

### 评估结果

- **质量提升情况：**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/146fcd3c-49e2-40b7-852e-5ec97aa00eaf.png" alt="QualityGains" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

- **评级提升情况：**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/0e923b0d-e7e0-430c-83c2-97458cfa67e8.png" alt="Grade" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

- **百分位数图表：**
<img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/030d6eca-efef-40bd-a4a8-27656e6e9326.png" alt="Percentile Chart" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />