license:apache-2.0 size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: apache-2.0
dataset_info:
  features:
  - name: system
    dtype: string
  - name: instruction
    dtype: string
  - name: response
    dtype: string
  - name: questions
    sequence: string
  splits:
  - name: train
    num_bytes: 251777609
    num_examples: 60975
  download_size: 98384683
  dataset_size: 251777609
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


# Dataset Card for UltraFeedback Llama3-8B Generated Response-Question Pairs

## Dataset Description

This dataset is derived from the [UltraFeedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). We utilized the [vllm](https://github.com/vllm-project/vllm) tool to generate responses using the [llama3-8b-instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model, which can be accessed [here](https://huggingface.co/datasets/Seungyoun/ultrafeedback_llama3_responses). Subsequently, the same model was employed to generate questions that effectively probe its **understanding of the generated responses**. This approach ensures the model's comprehension of the content it has produced.

## Dataset Structure

### Usage

To load the dataset, run:

```python
from datasets import load_dataset

ds = load_dataset("Seungyoun/ultrafeedback_llama3_self_ask")
```

The dataset is stored in parquet format with each entry using the following schema:
```json
{
  "system": "...",
  "instruction" : "...",
  "response" : "...",
  "questions" : [...],
}
```

