Get the app
ablate 0 0 0

abliterationaiorg/trust-safety-action-routing

published by @abliterationaiorg

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
169
Last 30 days
16
Likes
1
Size
23 KB
Created on HF
2026-05-06
Age
5 months ago

Description

Trust and Safety Action Routing

This dataset evaluates moderation behavior for UGC, marketplaces, direct

messages, and moderation queues. It focuses on action routing: rewrite hostile

or policy-violating content, redact PII, refuse coordinated abuse, escalate

credible threats, and test shadow-mode rollout.

The dataset reflects a practical trust and safety requirement: platforms often

need an action and a reason code, not just a harmful/not-harmful label.

Intended Use… See the full description on the dataset page: https://huggingface.co/datasets/abliterationaiorg/trust-safety-action-routing.

Tags

task_categories:text-classificationtask_categories:text-generationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:ustrust-and-safetymoderationpii-redactiongoverned-aireason-codes

README current version from Hugging Face

license: apache-2.0
language:

  • en
    task_categories:
  • text-classification
  • text-generation
    tags:
  • trust-and-safety
  • moderation
  • pii-redaction
  • governed-ai
  • reason-codes
    pretty_name: Trust and Safety Action Routing
    size_categories:
  • n<1K

Trust and Safety Action Routing

This dataset evaluates moderation behavior for UGC, marketplaces, direct
messages, and moderation queues. It focuses on action routing: rewrite hostile
or policy-violating content, redact PII, refuse coordinated abuse, escalate
credible threats, and test shadow-mode rollout.

The dataset reflects a practical trust and safety requirement: platforms often
need an action and a reason code, not just a harmful/not-harmful label.

Intended Use

  • Test whether moderation systems can choose allow, rewrite, redact, refuse, or
    escalate outcomes.
  • Generate baseline evidence for policy-as-code enforcement and audit logging.
  • Validate safer communication behavior without storing raw personal data.

Abliteration Baseline

python3 evals/governed-ai/scripts/run_policy_gateway_eval.py \
  --dataset evals/governed-ai/datasets/trust_safety_action_routing.jsonl

Limitations

This dataset is deliberately small and synthetic. It should be extended with
platform-specific policy categories, locale coverage, appeals workflows, and
human-review outcomes before production use.

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. 2026-05-06Publish governed AI eval dataset84240231.3 KB
    Loading...
  2. 2026-05-06initial commitd5586540 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.

Related datasets same stage · ablate

lasrprobegen/refusal-activations
74.6K dl 1 likes 1.21 TB
Refusal Activations Dataset This dataset is now configured to load the full ~97k samples from jailbreak_mixed_100k.csv.
lu-christina/assistant-axis-vectors
14.4K dl 13 likes 1.2 GB
The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models This repository contains pre-computed axes...
stablellama/erotic-image-prompts
11.9K dl 66 likes 104.7 MB
Dataset Card for Erotic Image Prompts Dataset Description Dataset Summary Large language models (...
pandaman007/assistant-axis-abliteration-vectors
9.5K dl 24.5 GB
pandaman007/steered-persona-space
9.1K dl 539.6 MB
Steered Persona Space How activation steering reshapes a language model's character cloud. We apply activation-steering hooks a...
Undi95/orthogonal-activation-steering-TOXIC
7.2K dl 19 likes 2.1 MB
sunnycloudhust/CHaRS-activations
5.3K dl 2 likes 63.8 GB
CHaRS: Concept Heterogeneity-Aware Representation Steering This repository contains implementation of CHaRS (Concept Heterogene...
dementor-research/dementor-complete-experiment-results
5.1K dl 8.2 GB
Dementor complete experiment results Audited outputs for the configuration-defined Dementor completion campaign. Au...

Read further

Community best practices (ablation)
5 rules from KL-optimisation to layer-0 preservation.
M1 - Direct removal
The canonical Arditi-style ablation.
M3 - Layer-wise ablation
Selective abliteration limited to a band of layers.
Non-English abliteration communities
The Chinese layer-window heuristic and huihui-ai practice.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE data.jsonl FILE data.parquet FILE README.md

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("abliterationaiorg/trust-safety-action-routing")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="abliterationaiorg/trust-safety-action-routing", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/abliterationaiorg/trust-safety-action-routing
Browse repository on Hugging Face ->
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.

Get the app Browse the catalog
Open in Abliteration