YYYYYYibo/ultrafeedback_binarized_doff_no_golden_vllm_part_3_mini_1

---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: reference_response
dtype: string
- name: resp0
dtype: string
- name: resp1
dtype: string
- name: resp2
dtype: string
- name: resp3
dtype: string
splits:
- name: train_prefs
num_bytes: 77283394
num_examples: 5283
download_size: 44424093
dataset_size: 77283394
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
# Dataset Card for "ultrafeedback_binarized_doff_no_golden_vllm_part_3_mini_1"

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

The dataset includes multiple features such as prompt, prompt_id, chosen, rejected, messages, score_chosen, score_rejected, reference_response, and multiple responses (resp0 to resp3). Each feature has its data type, such as string or float64. The dataset is split into a training set (train_prefs) with 5283 samples. The dataset configuration is named default, with data file paths at data/train_prefs-*.

原始信息汇总

数据集概述

数据集名称

• 名称: ultrafeedback_binarized_doff_no_golden_vllm_part_3_mini_1

数据集特征

• prompt: 字符串类型
• prompt_id: 字符串类型
• chosen: 列表类型，包含
  • content: 字符串类型
  • role: 字符串类型
• rejected: 列表类型，包含
  • content: 字符串类型
  • role: 字符串类型
• messages: 列表类型，包含
  • content: 字符串类型
  • role: 字符串类型
• score_chosen: 浮点数类型
• score_rejected: 浮点数类型
• reference_response: 字符串类型
• resp0: 字符串类型
• resp1: 字符串类型
• resp2: 字符串类型
• resp3: 字符串类型

数据集分割

• train_prefs:
  • num_bytes: 77283394
  • num_examples: 5283

数据集大小

• download_size: 44424093
• dataset_size: 77283394

配置

• config_name: default
• data_files:
  • split: train_prefs
  • path: data/train_prefs-*