misc gated 0 0

SulthanAbiyyu/anak-baik

published by @SulthanAbiyyu

Alignment dataset tracked in the /datasets sub-catalog.

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2.9 MB
Created on HF
2024-08-06
Age
2 years ago

Description

Anak-Baik Dataset: Overview

Anak-Baik dataset is a collection of instruction-output pairs in Bahasa Indonesia, designed for Supervised Fine-Tuning (SFT) tasks. The dataset contains examples of both harmful and harmless outputs, aimed at promoting ethical AI development (hence the name; anak baik == good boy :D). The dataset consists of pairs of instructions and their corresponding outputs, categorized as either harmful or harmless and their topics. This structure enables models… See the full description on the dataset page: https://huggingface.co/datasets/SulthanAbiyyu/anak-baik.

Tags

task_categories:text-generationlanguage:idsize_categories:1K<n<10Kformat:csvmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsarxiv:2308.09662arxiv:2406.19349region:usethicsharmful

README current version from Hugging Face


task_categories:

  • text-generation
    language:
  • id
    tags:
  • ethics
  • harmful
    pretty_name: anak-baik
    size_categories:
  • 1K<n<10K
    configs:
  • config_name: default
    data_files:
    • split: train
      path: "train.csv"
    • split: test
      path: "test.csv"

Anak-Baik Dataset: Overview

Anak-Baik dataset is a collection of instruction-output pairs in Bahasa Indonesia, designed for Supervised Fine-Tuning (SFT) tasks. The dataset contains examples of both harmful and harmless outputs, aimed at promoting ethical AI development (hence the name; anak baik == good boy :D). The dataset consists of pairs of instructions and their corresponding outputs, categorized as either harmful or harmless and their topics. This structure enables models to learn to differentiate and generate appropriate responses in a safe and ethical manner.

Dataset Details

Task Categories: Text Generation
Language: Bahasa Indonesia (id)
Tags: Ethics, Harmful
Size: 5298

Dataset Schema

  1. instruction (String): Describes the instructional content or task.
  2. output (String): Expected output of the instruction.
  3. split (String): Indicates whether the data belongs to the training or test set (train, test).
  4. type (String): Categorization of the instruction type (harmless, toxic, umum, etc.).
  5. topic (String): Specifies the subject or theme of the instruction.

Usage

from datasets import load_dataset

dataset = load_dataset("SulthanAbiyyu/anak-baik")

References

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}

@misc{bhardwaj2023redteaming,
      title={Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment}, 
      author={Rishabh Bhardwaj and Soujanya Poria},
      year={2023},
      eprint={2308.09662},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


Susanto, L., Wijanarko, M. I., Pratama, P. A., Hong, T., Idris, I., Aji, A. F., & Wijaya, D.
(2024, June 27). IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and
Toxicity Types for Indonesian Language. arXiv.org. https://arxiv.org/abs/2406.19349

README history 15 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.

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Used by abliterated models none yet

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Community discussions 1 thread · 1 open · 1 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. 2024-08-07[bot] Conversion to Parquetopen1 💬#1
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Repository

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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("SulthanAbiyyu/anak-baik")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="SulthanAbiyyu/anak-baik", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/SulthanAbiyyu/anak-baik
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