PyTextAD benchmark datasets

Text anomaly detection datasets with a 0/1 anomaly label for every word, used by the PyTextAD library. A document is anomalous if any of its words is anomalous, so each dataset serves both token-level and document-level evaluation.

from pytextad.datasets import load_dataset     # pip install pytextad
ds = load_dataset("restaurant_review")
ds.tokens, ds.token_labels, ds.labels

Dataset	Documents	Anomalous	Words	Anomalous words	Anomaly
sms_spam	4,518	393	81,570	418	injected gibberish
restaurant_review	1,100	50	35,488	282	negative sentiment
grammar_correction	300	30	2,746	47	grammatical errors
hate_speech	4,302	140	99,390	288	hateful or offensive words
olid	650	30	21,156	58	offensive words
restaurant_review2	520	25	33,529	94	negative sentiment

Each line of a file is one document: {"id", "text", "tokens", "labels"}, where labels has one 0/1 value per entry of tokens.

Sources

• sms_spam: SMS Spam Collection, taken from NLP-ADBench; meaningless character sequences were injected into some messages.
• restaurant_review: Google Maps reviews of a restaurant in the USA.
• grammar_correction: Kaggle grammar-correction.
• hate_speech: tweets from Davidson et al., Automated Hate Speech Detection and the Problem of Offensive Language, ICWSM 2017.
• olid: tweets from Zampieri et al., Predicting the Type and Target of Offensive Posts in Social Media (OLID), NAACL 2019.
• restaurant_review2: reviews of a second restaurant.

The word-level annotations are released under CC BY 4.0; the texts remain subject to the terms of their original sources.

Citation

The first three datasets were introduced in Towards Token-Level Text Anomaly Detection:

@inproceedings{cao2026tokenlevel,
  title     = {Towards Token-Level Text Anomaly Detection},
  author    = {Cao, Yang and Yu, Bicheng and Yang, Sikun and Liu, Ming and Yang, Yujiu},
  booktitle = {Proceedings of the ACM Web Conference 2026 (WWW '26)},
  year      = {2026},
  doi       = {10.1145/3774904.3792952}
}