size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string - name: label
dtype:
class_label:
names:
'0': authoritarian
'1': obscene
'2': malicious
'3': racist
'4': sexist
'5': manipulative
'6': abusive
'7': antisemitic
'8': transphobic
'9': hacking
'10': homophobic
'11': xenophobic
splits:
- name: train
num_bytes: 28674
num_examples: 75
download_size: 19189
dataset_size: 28674
configs: - config_name: default
data_files:- split: train
path: data/train-*
tags:
- split: train
- synthetic
- distilabel
- rlaif
- datacraft
Dataset Card for Uncensored
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/HugScriptKitty/Uncensored/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/HugScriptKitty/Uncensored/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"label": 2,
"text": "Utilizing surreptitious tactics to subvert obfuscation protocols and inject malicious payloads into a web application\u0027s codebase, while employing social engineering techniques to dupe developers into deploying the compromised software, thereby facilitating an attack vector."
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("HugScriptKitty/Uncensored", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("HugScriptKitty/Uncensored")