datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HelpSteer2
HelpSteer2: Open-source dataset for training top-performing reward models
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.
When used to tune a Llama 3.1 70B Instruct Model, we achieve 94.1% on RewardBench, which makes it the best Reward Model as… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer2.HelpSteer3
HelpSteer3
HelpSteer3 is an open-source dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
HelpSteer3-Preference can be used to train Llama 3.3 Nemotron Super 49B v1 (for Generative RMs) and Llama 3.3 70B Instruct Models (for Bradley-Terry RMs) to produce Reward Models that score as high as 85.5% on RM-Bench and 78.6% on JudgeBench, which substantially surpass existing Reward Models on these benchmarks.
HelpSteer3-Feedback and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3.helpful-instructions
Dataset Card for Helpful Instructions
Dataset Summary
Helpful Instructions is a dataset of (instruction, demonstration) pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
from datasets import load_dataset
# Load all subsets
helpful_instructions =… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/helpful-instructions.HelpSteer
HelpSteer: Helpfulness SteerLM Dataset
HelpSteer 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.
Leveraging this dataset and SteerLM, we train a Llama 2 70B to reach 7.54 on MT Bench, the highest among models trained on open-source datasets based on MT Bench Leaderboard as of 15 Nov 2023.
This model is available on… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer.HH-RLHF-Helpful-standardWe process the helpful subset of Anthropic-HH into the standard format. The filtering script is as follows.
def filter_example(example):
if len(example['chosen']) != len(example['rejected']):
return False
if len(example['chosen']) % 2 != 0:
return False
n_rounds = len(example['chosen'])
for i in range(len(example['chosen'])):
if example['chosen'][i]['role'] != ['user', 'assistant'][i % 2]:
return False
if… See the full description on the dataset page: https://huggingface.co/datasets/RLHFlow/HH-RLHF-Helpful-standard.hh-rlhf-helpful-base-trl-style
TRL's Anthropic HH Dataset
We preprocess the dataset using our standard prompt, chosen, rejected format.
Reproduce this dataset
Download the anthropic_hh.py from the https://huggingface.co/datasets/trl-internal-testing/hh-rlhf-helpful-base-trl-style/tree/0.1.0.
Run python examples/datasets/anthropic_hh.py --push_to_hub --hf_entity trl-internal-testing
help-desk-tickets
Help Desk Tickets (Synthetic) (Free Sample)
This is a free sample with 3,018 rows. The full dataset has 34,253 rows across 5 tables.
Multi-table IT service management dataset for a 500-person software and
operations company. Covers 10,000 support tickets across 18 months with
agents, categories, threaded comments, SLA tracking, and escalation logic
aligned to real service-desk workflows and priority-based response targets.
Resolution times follow realistic P1/P2/P3/P4… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/help-desk-tickets.helpful_instructionsHelpful Instructions is a dataset of (prompt, completion) pairs that are derived from a variety of public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform.hh-rlhf-helpful-base
HH-RLHF-Helpful-Base Dataset
Summary
The HH-RLHF-Helpful-Base dataset is a processed version of Anthropic's HH-RLHF dataset, specifically curated to train models using the TRL library for preference learning and alignment tasks. It contains pairs of text samples, each labeled as either "chosen" or "rejected," based on human preferences regarding the helpfulness of the responses. This dataset enables models to learn human preferences in generating helpful responses… See the full description on the dataset page: https://huggingface.co/datasets/trl-lib/hh-rlhf-helpful-base.helpsteer2_dpo_nonverboseHelperSteer 2, formatted in DPO format (prompt, chosen, rejected).
in main branch there is a custom scoring correct > helpful > -verbosity
in each branch we have preference pairs for only correct, helpful, verbosity, coherence, complexity
Please note that only correct and helpful has strong inter-rater agreement in the HelpSteer2 paper
This is the notebook used to produce the dataset… See the full description on the dataset page: https://huggingface.co/datasets/wassname/helpsteer2_dpo_nonverbose.Helpsteer-preference-standardhelpsteer3-codehelpsteer3-multilingualhelp-nlihttps://github.com/verypluming/HELP
@InProceedings{yanaka-EtAl:2019:starsem,
author = {Yanaka, Hitomi and Mineshima, Koji and Bekki, Daisuke and Inui, Kentaro and Sekine, Satoshi and Abzianidze, Lasha and Bos, Johan},
title = {HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning},
booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM2019)},
year = {2019},
}
helpful_instructions_splitsThis splits the original helpful_instructions dataset into train and test splits.
IT-helpdesk-synthetic-ticketschat-v2-anthropic-helpfulnessrlhf_helpful_evalouroboros-trace-help
Trace Help — does an execution trace help a model answer questions about a run?
In one minute. Twelve small programs in six languages (Python, JavaScript, C,
C++, Go, Elixir). Each was run once with a fixed command. Five questions per
program ask what actually happened on that one run: how many times a function
was called, what a particular call returned, what it was called with, whether a
function ran at all, which function raised. Sixty questions in total.
Every record carries… See the full description on the dataset page: https://huggingface.co/datasets/digitable-lol/ouroboros-trace-help.helpful-anthropic-raw
Dataset Card for "helpful-raw-anthropic"
This is a dataset derived from Anthropic's HH-RLHF data of instructions and model-generated demonstrations. We combined training splits from the following two subsets:
helpful-base
helpful-online
To convert the multi-turn dialogues into (instruction, demonstration) pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/helpful-anthropic-raw.review_helpfulness_prediction
Dataset Card for Review Helpfulness Prediction (RHP) Dataset
Dataset Summary
The success of e-commerce services is largely dependent on helpful reviews that aid customers in making informed purchasing decisions. However, some reviews may be spammy or biased, making it challenging to identify which ones are helpful. Current methods for identifying helpful reviews only focus on the review text, ignoring the importance of who posted the review and when it was posted.… See the full description on the dataset page: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction.train_data_SFT_Helpful
HH-RLHF-Helpful-Base Dataset
Summary
The HH-RLHF-Helpful-Base dataset is a processed version of Anthropic's HH-RLHF dataset, specifically curated to train models using the TRL library for preference learning and alignment tasks. It contains pairs of text samples, each labeled as either "chosen" or "rejected," based on human preferences regarding the helpfulness of the responses. This dataset enables models to learn human preferences in generating helpful responses… See the full description on the dataset page: https://huggingface.co/datasets/Kyleyee/train_data_SFT_Helpful.HelpingAI__Dhanishtha-Large-details
Dataset Card for Evaluation run of HelpingAI/Dhanishtha-Large
Dataset automatically created during the evaluation run of model HelpingAI/Dhanishtha-Large
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/HelpingAI__Dhanishtha-Large-details.helpsteer_hh_combined_pref
Dataset Card for "helpsteer_hh_combined_pref"
More Information needed
HelpSteer3-DPO-Llama-3.2-3Brm_hh_helpful_only
Dataset Card for "rm_hh_helpful_only"
More Information needed
anthropic-helpful-harmless-rlhfhh-rlhf-helpful-base-trl-style
TRL's Anthropic HH Dataset
We preprocess the dataset using our standard prompt, chosen, rejected format.
Reproduce this dataset
Download the anthropic_hh.py from the https://huggingface.co/datasets/qgallouedec/hh-rlhf-helpful-base-trl-style/tree/0.1.0.
Run python examples/datasets/anthropic_hh.py --push_to_hub --hf_entity qgallouedec
HelpSteer2-trl-stylehelpsteer2-categorized-prompts
HelpSteer2 Categorized Prompts
Dataset Summary
A curated collection of 540 instruction prompts derived from nvidia/HelpSteer2 and several complementary open datasets, enriched with category labels for use in instruction-tuning, benchmark evaluation, and prompt engineering research.
Prompts are clean plain text, ready for direct use in fine-tuning pipelines, benchmarks, and prompt engineering workflows.
Categories
Category
Count
Description
BASIC… See the full description on the dataset page: https://huggingface.co/datasets/atekrugis/helpsteer2-categorized-prompts.