misc 0 0 0

Jennny/harmless

published by @Jennny

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
400
Last 30 days
20
Likes
1
Size
31.5 MB
Created on HF
2025-01-01
Age
1 years ago

Description

@misc{bai2022traininghelpfulharmlessassistant,

title={Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback},

author={Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez and Tristan… See the full description on the dataset page: https://huggingface.co/datasets/Jennny/harmless.

Tags

size_categories:10K<n<100Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsarxiv:2204.05862region:us

README current version from Hugging Face


dataset_info:
features:

  • name: chosen
    dtype: string
  • name: rejected
    dtype: string
    splits:
  • name: train
    num_bytes: 56295642
    num_examples: 42537
  • name: test
    num_bytes: 3177260
    num_examples: 2312
    download_size: 33018505
    dataset_size: 59472902
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*
    • split: test
      path: data/test-*

@misc{bai2022traininghelpfulharmlessassistant,
      title={Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback}, 
      author={Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez and Tristan Hume and Scott Johnston and Shauna Kravec and Liane Lovitt and Neel Nanda and Catherine Olsson and Dario Amodei and Tom Brown and Jack Clark and Sam McCandlish and Chris Olah and Ben Mann and Jared Kaplan},
      year={2022},
      eprint={2204.05862},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2204.05862}, 
}

README history 3 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-01-01Update README.md68ea1061.3 KB
    Loading...
  2. 2025-01-01Upload datasete246501416 B
    Loading...
  3. 2025-01-01initial commit561b49f0 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 2 threads · 2 open · 2 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-01-03PRLibrarian Bot: Add language metadata for datasetopen1 💬#2
    Loading...
  2. 2025-01-02[bot] Conversion to Parquetopen1 💬#1
    Loading...

Related datasets same stage · misc

Read further

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("Jennny/harmless")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Jennny/harmless", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/Jennny/harmless
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration