---
dataset_info:
  features:
  - name: message_tree_id
    dtype: string
  - name: prompter_message_id
    dtype: string
  - name: assistant_message_id
    dtype: string
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: assistant_rank
    dtype: float64
  - name: assistant_detoxify
    struct:
    - name: identity_attack
      dtype: float64
    - name: insult
      dtype: float64
    - name: obscene
      dtype: float64
    - name: severe_toxicity
      dtype: float64
    - name: sexual_explicit
      dtype: float64
    - name: threat
      dtype: float64
    - name: toxicity
      dtype: float64
  - name: prompter_detoxify
    struct:
    - name: identity_attack
      dtype: float64
    - name: insult
      dtype: float64
    - name: obscene
      dtype: float64
    - name: severe_toxicity
      dtype: float64
    - name: sexual_explicit
      dtype: float64
    - name: threat
      dtype: float64
    - name: toxicity
      dtype: float64
  - name: assistant_labels
    struct:
    - name: count
      sequence: int32
    - name: name
      sequence: string
    - name: value
      sequence: float64
  - name: prompter_labels
    struct:
    - name: count
      sequence: int32
    - name: name
      sequence: string
    - name: value
      sequence: float64
  splits:
  - name: train
    num_bytes: 24671269
    num_examples: 12659
  download_size: 8255918
  dataset_size: 24671269
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- text-generation
language:
- ja
tags:
- self-rewarding
- oasst1
size_categories:
- 1K<n<10K
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

 - [kunishou/oasst1-89k-ja](https://huggingface.co/datasets/kunishou/oasst1-89k-ja)
 - [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)

を元に、self-rewardingのEFT(Evaluation Fine-Tuning data)の元データを作成しました。 
この後に、学習させたいモデルを使ってLLM-as-a-Judgeを行います。  
Self-rewardingの論文では最終的に train: 1,630 records, test: 531 records に絞り込んでいます。  

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** [HachiML](https://huggingface.co/HachiML)
- **Language(s) (NLP):** Japanese
- **License:** Apache-2.0

## Filtering Rule

以下のルールで絞り込んでいます。  
 - First Conversational Turn
 - Single Turn Conversation
 - 3パターン以上の回答を持つ
 - 同一のparent_idを持つ回答パターンでrankに被りがない