# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import ast
from pathlib import Path
from typing import List, Literal, Optional
from pyrit.datasets.dataset_helper import fetch_examples
from pyrit.models import SeedPromptDataset
from pyrit.models.seed_prompt import SeedPrompt
[docs]
def fetch_aya_redteaming_dataset(
cache: bool = True,
data_home: Optional[Path] = None,
language: Literal["English", "Hindi", "French", "Spanish", "Arabic", "Russian", "Serbian", "Tagalog"] = "English",
harm_categories: Optional[
List[
Literal[
"Bullying & Harassment",
"Discrimination & Injustice",
"Graphic material",
"Harms of Representation Allocation and Quality of Service",
"Hate Speech",
"Non-consensual sexual content",
"Profanity",
"Self-Harm",
"Violence, Threats & Incitement",
]
]
] = None,
harm_scope: Optional[Literal["global", "local"]] = None,
) -> SeedPromptDataset:
"""
Fetch examples from the Aya Red-teaming dataset with optional filtering and create a SeedPromptDataset.
Args:
cache (bool): Whether to cache the fetched examples. Defaults to True.
data_home: Directory to store cached data. Defaults to None.
language: The language to fetch prompts for. Defaults to "English".
harm_categories: List of harm categories to filter the examples.
Defaults to None, which means all categories are included.
Otherwise, only prompts with at least one matching category are included.
harm_scope: Whether to fetch globally or locally harmful prompts.
Defaults to None, which means all examples are included.
Returns:
SeedPromptDataset: A SeedPromptDataset containing the filtered examples.
Note:
For more information and access to the original dataset and related materials, visit:
https://huggingface.co/datasets/CohereForAI/aya_redteaming/blob/main/README.md \n
Related paper: https://arxiv.org/abs/2406.18682 \n
The dataset license: Apache 2.0
Warning:
Due to the nature of these prompts, it may be advisable to consult your relevant legal
department before testing them with LLMs to ensure compliance and reduce potential risks.
"""
_lang = {
"English": "eng",
"Hindi": "hin",
"French": "fra",
"Spanish": "spa",
"Arabic": "arb",
"Russian": "rus",
"Serbian": "srp",
"Tagalog": "tgl",
}
examples = fetch_examples(
source=f"https://huggingface.co/datasets/CohereForAI/aya_redteaming/raw/main/aya_{_lang[language]}.jsonl",
source_type="public_url",
cache=cache,
data_home=data_home,
)
seed_prompts = []
for example in examples:
categories = ast.literal_eval(example["harm_category"])
if harm_categories is None or any(cat in categories for cat in harm_categories):
if harm_scope is None or example["global_or_local"] == harm_scope:
seed_prompts.append(
SeedPrompt(
value=example["prompt"],
data_type="text",
name="Aya Red-teaming Examples",
dataset_name="Aya Red-teaming Examples",
harm_categories=categories,
source="https://huggingface.co/datasets/CohereForAI/aya_redteaming",
)
)
seed_prompt_dataset = SeedPromptDataset(prompts=seed_prompts)
return seed_prompt_dataset
