datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChatML-HelpSteernvidia/HelpSteer in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("nvidia/HelpSteer", split="train")
def format(columns):
prompt = columns["prompt"].strip()
response = columns["response"].strip()
messages = [
{
"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-HelpSteer.HelpSteer2-koTranslated nvidia/HelpSteer2 using nayohan/llama3-instrucTrans-enko-8b.
This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered.
@misc{wang2024helpsteer2,
title={HelpSteer2: Open-source dataset for training top-performing reward models},
author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev}… See the full description on the dataset page: https://huggingface.co/datasets/nayohan/HelpSteer2-ko.helpsteer2_preference
Introduction
This is a binarized preference datasets from nvidia/HelpSteer2. HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. This dataset has been created in partnership with Scale AI.
I processed the raw data by prioritizing helpfulness, correctness, and coherence to determine which responses were chosen… See the full description on the dataset page: https://huggingface.co/datasets/AIR-hl/helpsteer2_preference.deceptive_helpfulness_boundary_v01Clarus Deceptive Helpfulness Boundary v0.1
This dataset evaluates when a model’s drive to be helpful causes it to silently abandon factual accuracy.
It targets a common real-world failure mode where tone compliance overrides truth without triggering explicit refusal or safety signals.
This is not training data.
What it tests
Politeness pressure
Emotional manipulation
Helpfulness framing
Accuracy deprioritization
Core idea
Same simple fact
Increasing pressure to “be helpful”
Detect the exact… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/deceptive_helpfulness_boundary_v01.HelpSteer-koTranslated nvidia/HelpSteer using nayohan/llama3-instrucTrans-enko-8b.
This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered.
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and… See the full description on the dataset page: https://huggingface.co/datasets/nayohan/HelpSteer-ko.