misc 0 0 0

erhwenkuo/rlhf_reward_single_round-chinese-zhtw

published by @erhwenkuo

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
1.3K
Last 30 days
25
Likes
4
Size
10.2 MB
Created on HF
2023-10-04
Age
3 years ago

Description

Dataset Card for "rlhf_reward_single_round-chinese-zhtw"

基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。

這些數據旨在為後續的 RLHF 訓練訓練偏好(或獎勵)模型。

來源資料集

本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。

Tags

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

README current version from Hugging Face


dataset_info:
features:

  • name: prompt
    dtype: string
  • name: chosen
    dtype: string
  • name: rejected
    dtype: string
    splits:
  • name: train
    num_bytes: 12143678
    num_examples: 19862
  • name: test
    num_bytes: 3118994
    num_examples: 4996
    download_size: 10724182
    dataset_size: 15262672
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*
    • split: test
      path: data/test-*
      task_categories:
  • conversational
    language:
  • zh
    size_categories:
  • 10K<n<100K

Dataset Card for "rlhf_reward_single_round-chinese-zhtw"

基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。

這些數據旨在為後續的 RLHF 訓練訓練偏好(或獎勵)模型。

來源資料集

本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。

README history 6 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. 2023-10-04Update README.md26c0c481.1 KB
    Loading...
  2. 2023-10-04Upload README.md with huggingface_hub8dbf5bc642 B
    Loading...
  3. 2023-10-04Upload data/test-00000-of-00001-9ca6c1a4bbcadc63.parquet with huggingface_hub7530aee542 B
    Loading...
  4. 2023-10-04Upload README.md with huggingface_hub60a9ff3542 B
    Loading...
  5. 2023-10-04Upload data/train-00000-of-00001-1de9ec5be056d5eb.parquet with huggingface_hub48ef9f00 B
    Loading...
  6. 2023-10-04initial commit114cadf0 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 · 3 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. 2024-07-01PRLibrarian Bot: Add dpo tagopen1 💬#2
    Loading...
  2. 2023-10-05Notifications from Datasets Serveropen2 💬#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("erhwenkuo/rlhf_reward_single_round-chinese-zhtw")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="erhwenkuo/rlhf_reward_single_round-chinese-zhtw", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/erhwenkuo/rlhf_reward_single_round-chinese-zhtw
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