File size: 2,842 Bytes
73cc8d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
_EVAL_SPLITS = ["test"]
class ToxicChatClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="ToxicChatClassification",
description="""This dataset contains toxicity annotations on 10K user
prompts collected from the Vicuna online demo. We utilize a human-AI
collaborative annotation framework to guarantee the quality of annotation
while maintaining a feasible annotation workload. The details of data
collection, pre-processing, and annotation can be found in our paper.
We believe that ToxicChat can be a valuable resource to drive further
advancements toward building a safe and healthy environment for user-AI
interactions.
Only human annotated samples are selected here.""",
reference="https://aclanthology.org/2023.findings-emnlp.311/",
dataset={
"path": "lmsys/toxic-chat",
"name": "toxicchat0124",
"revision": "3e0319203c7162b9c9f8015b594441f979c199bc",
},
type="Classification",
category="s2s",
eval_splits=_EVAL_SPLITS,
eval_langs=["eng-Latn"],
main_score="accuracy",
date=("2023-10-26", "2024-01-31"),
form=["written"],
domains=["Constructed"],
task_subtypes=["Sentiment/Hate speech"],
license="cc-by-4.0",
socioeconomic_status="high",
annotations_creators="expert-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@misc{lin2023toxicchat,
title={ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation},
author={Zi Lin and Zihan Wang and Yongqi Tong and Yangkun Wang and Yuxin Guo and Yujia Wang and Jingbo Shang},
year={2023},
eprint={2310.17389},
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
n_samples={"test": 1427},
avg_character_length={"test": 189.4},
)
def dataset_transform(self):
keep_cols = ["user_input", "toxicity"]
rename_dict = dict(zip(keep_cols, ["text", "label"]))
remove_cols = [
col
for col in self.dataset[_EVAL_SPLITS[0]].column_names
if col not in keep_cols
]
self.dataset = self.dataset.rename_columns(rename_dict)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"]
)
# only use human-annotated data
self.dataset = self.dataset.filter(lambda x: x["human_annotation"])
self.dataset = self.dataset.remove_columns(remove_cols)
|