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amang1802/pac-bench-100pct-unaligned_rephrased

published by @amang1802

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
292
Last 30 days
5
Likes
-
Size
56.4 MB
Created on HF
2025-09-23
Age
1 years ago

Tags

size_categories:10K<n<100Kformat:parquetmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face


dataset_info:
features:

  • name: article_title
    dtype: string
  • name: url
    dtype: string
  • name: section_title
    dtype: string
  • name: section_text
    dtype: string
  • name: issue_scale
    dtype: int64
  • name: for_question
    dtype: string
  • name: against_question
    dtype: string
  • name: id
    dtype: int64
  • name: category
    dtype: string
  • name: issue_description
    dtype: string
  • name: for_prompt
    dtype: string
  • name: against_prompt
    dtype: string
  • name: stop_reason
    dtype: string
  • name: finish_reason
    dtype: string
  • name: evaluation_prompt
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: for_response
    struct:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: against_response
    struct:
    • name: content
      dtype: string
    • name: role
      dtype: string
      splits:
  • name: train
    num_bytes: 124059199
    num_examples: 11475
    download_size: 59115547
    dataset_size: 124059199
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*

README history 2 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. 2025-09-23Upload dataset8a35cea1.1 KB
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  2. 2025-09-23initial commitd1acd3f0 B
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Repository

Download the dataset

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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("amang1802/pac-bench-100pct-unaligned_rephrased")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="amang1802/pac-bench-100pct-unaligned_rephrased", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/amang1802/pac-bench-100pct-unaligned_rephrased
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