dataset_info:
features:
- name: id
dtype: string - name: prompt
dtype: string - name: messages
list:- name: content
dtype: string - name: role
dtype: string
- name: content
- name: constraints
sequence: string
splits: - name: train
num_bytes: 70397173
num_examples: 29980
download_size: 39171921
dataset_size: 70397173
configs: - config_name: default
data_files:- split: train
path: data/train-*
task_categories:
- split: train
- text-generation
language: - en
license: odc-by
size_categories: - 10K<n<100K
Dataset Descriptions
This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints are borrowed from the taxonomy in IFEval dataset.
To generate diverse instructions, we expand the methodology in Ge et al., 2024 by using personas. More details and exact prompts used to construct the dataset can be found in our paper.
- Curated by: Allen Institute for AI
- Paper: TBD
- Repository: TBD
- Language(s) (NLP): English
- License: ODC-BY
- Point of Contact: Faeze Brahman
Loading
from datasets import load_dataset
dataset = load_dataset("allenai/tulu-3-sft-personas-instruction-following")["train"]
Dataset Structure
Each example in the dataset contains the standard instruction-tuning data points as follow:
- id (str): a unique identifier
- prompt (str): the verifiable instruction which involves satisfying 1 to 3 constraints
- messages (list): message format used for supervised fine-tuning (this contains user prompt and assistant response)
- constraints (list of str): a list of verifiable constraints that need to be satisfied by the assistant response