DIBT/aya_dutch_dpo_raw

---
size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: targets
dtype: string
- name: language
dtype: string
- name: language_code
dtype: string
- name: annotation_type
dtype: string
- name: user_id
dtype: string
- name: generation_model
dtype: string
- name: generation
dtype: string
- name: predicted_generation_language
dtype: string
- name: predicted_generation_language_score
dtype: float64
- name: generations
sequence: string
- name: generation_models
sequence: string
- name: model_name
dtype: string
- name: ratings
sequence: int64
- name: rationales
sequence: string
splits:
- name: train
num_bytes: 3530439
num_examples: 1200
download_size: 1847668
dataset_size: 3530439
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

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<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
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</p>

# Dataset Card for aya_dutch_dpo_raw

This dataset has been created with [distilabel](https://distilabel.argilla.io/).

## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

<details><summary> Configuration: default </summary><hr>

```json
{
"annotation_type": "re-annotations",
"generation": "De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw.",
"generation_model": "meta-llama/Meta-Llama-3-70B-Instruct",
"generation_models": [
"aya",
"meta-llama/Meta-Llama-3-70B-Instruct"
],
"generations": [
"Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".",
"De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw."
],
"instruction": "Maak een complexere zin: Het fort werd gebouwd in de haven van Lahaina met koraalblokken.",
"language": "Dutch",
"language_code": "nld",
"model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
"predicted_generation_language": "nld_Latn",
"predicted_generation_language_score": 0.9995737671852112,
"ratings": [
4,
5
],
"rationales": [
"The text accurately provides a more complex sentence as requested, and the added details are correct (e.g., \"muren bekroond met 47 kanonnen\"). The sentence is well-structured and easy to follow. However, the model could be more confident in its language, and some parts feel slightly redundant (e.g., \"kadegebied bij Lahaina Harbor\").",
"The text provides a sophisticated and accurate sentence that not only meets the request but also adds valuable context about the significance of the fort\u0027s construction. The language is confident and precise, and the sentence is well-structured and engaging. The model demonstrates a thorough understanding of the topic and effectively conveys its knowledge without any noticeable errors or hallucinations."
],
"targets": "Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".",
"user_id": "ca908e583236b208e473e89dae5c7b7d3daf3662e2bbf6606f0702c718bb5c06"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("DIBT/aya_dutch_dpo_raw", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`:

```python
from datasets import load_dataset

ds = load_dataset("DIBT/aya_dutch_dpo_raw")
```

</details>

原始信息汇总

数据集概述

数据集基本信息

• 数据集名称: aya_dutch_dpo_raw
• 数据集大小:
  • 下载大小: 1847668字节
  • 数据集大小: 3530439字节
• 样本数量: 1200
• 数据集分类: 小于1K

数据集特征

• 特征名称 (数据类型):
  • instruction (string)
  • targets (string)
  • language (string)
  • language_code (string)
  • annotation_type (string)
  • user_id (string)
  • generation_model (string)
  • generation (string)
  • predicted_generation_language (string)
  • predicted_generation_language_score (float64)
  • generations (sequence: string)
  • generation_models (sequence: string)
  • model_name (string)
  • ratings (sequence: int64)
  • rationales (sequence: string)

数据集结构

• 配置名称: default
• 数据文件:
  • 分割: train
  • 路径: data/train-*

数据集加载示例

python from datasets import load_dataset

ds = load_dataset("DIBT/aya_dutch_dpo_raw")

数据集示例结构

json { "annotation_type": "re-annotations", "generation": "...", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": ["aya", "meta-llama/Meta-Llama-3-70B-Instruct"], "generations": ["...", "..."], "instruction": "...", "language": "Dutch", "language_code": "nld", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "nld_Latn", "predicted_generation_language_score": 0.9995737671852112, "ratings": [4, 5], "rationales": ["...", "..."], "targets": "...", "user_id": "ca908e583236b208e473e89dae5c7b7d3daf3662e2bbf6606f0702c718bb5c06" }