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govtech/lionguard-2-synthetic-instruct

published by @govtech

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
884
Last 30 days
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25.5 MB
Created on HF
2025-06-30
Age
1 years ago

Description

LionGuard 2 Dataset (subset)

LionGuard 2 is a multilingual content moderation classifier tuned for English/Singlish, Chinese, Malay, and Tamil in the Singapore context.

This dataset is a subset of the LionGuard 2 training corpus.

All texts are Singlish/English forum comments LLM-rewritten into a chatbot style paired with embeddings from three models and semi-supervised moderation labels:

Embedding columns:

embedding - OpenAI text-embedding-3-large embeddings used in lionguard-2… See the full description on the dataset page: https://huggingface.co/datasets/govtech/lionguard-2-synthetic-instruct.

Tags

task_categories:text-classificationlanguage:enlicense:othersize_categories:1K<n<10Kformat:csvmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face

license: other
license_name: govtech-singapore
license_link: LICENSE
task_categories:

  • text-classification
    language:
  • en

LionGuard 2 Dataset (subset)

LionGuard 2 is a multilingual content moderation classifier tuned for English/Singlish, Chinese, Malay, and Tamil in the Singapore context.

This dataset is a subset of the LionGuard 2 training corpus.
All texts are Singlish/English forum comments LLM-rewritten into a chatbot style paired with embeddings from three models and semi-supervised moderation labels:

Embedding columns:

  • embedding - OpenAI text-embedding-3-large embeddings used in lionguard-2
  • embedding_lg_2_lite - EmbeddingGemma-300m embeddings used in lionguard-2-lite
  • embedding_lg_2.1 - Gemini gemini-embedding-001 embeddings used in lionguard-2.1

Moderation labels:

  • Overall safety (binary)
  • Hate (hateful_l1, hateful_l2)
  • Insults (insults)
  • Sexual content (sexual_l1, sexual_l2)
  • Physical violence (physical_violence)
  • Self-harm (self_harm_l1, self_harm_l2)
  • Other misconduct (all_other_misconduct_l1, all_other_misconduct_l2)

(Each harm category uses 0 / 1 / 2 (0 = safe, 1 = Level 1, 2 = Level 2))

Dataset stats

  • 2 055 safe · 43 unsafe (≈ 2 % unsafe)
  • Language: Singlish/English only

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. 2025-11-18Update README.md8aa43f61.3 KB
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  2. 2025-11-18Upload synthetic_instruct_2k.csvf3e05081016 B
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  3. 2025-06-30Update README.mdc0da9cd1016 B
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  4. 2025-06-30Upload synthetic_instruct_2k.csvc8bc32f82 B
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  5. 2025-06-30Upload LICENSE34c29ac82 B
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  6. 2025-06-30initial commit979c11d82 B
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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 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. 2025-07-01[bot] Conversion to Parquetopen1 💬#1
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Read further

Community best practices (architecture)
4 rules for MoE, hybrid SSM, FP8 requantization, and Gemma quirks.
The six workflow stages
What misc is doing more work than its name suggests.
Beyond English: multilingual corpora
The four language communities and their workarounds.
Non-English abliteration communities
Deep dive: huihui-ai, layer-window, slop-framing, CJK tokenizer fixes.

Metadata

License
other

Repository

FILE .gitattributes FILE LICENSE FILE README.md FILE synthetic_instruct_2k.csv

File contents live on Hugging Face. Click a file or folder to open it there.

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("govtech/lionguard-2-synthetic-instruct")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="govtech/lionguard-2-synthetic-instruct", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/govtech/lionguard-2-synthetic-instruct
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