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---
language:
- en
license: mit
task_categories:
- text-classification
tags:
- anomaly-detection
- nlp
---
# NLP-ADBench: NLP Anomaly Detection Benchmark
**Paper:** [NLP-ADBench: NLP Anomaly Detection Benchmark](https://huggingface.co/papers/2412.04784)
**Code:** [https://github.com/USC-FORTIS/NLP-ADBench](https://github.com/USC-FORTIS/NLP-ADBench)
## Overview
**NLP-ADBench** is a comprehensive benchmarking tool designed for Anomaly Detection in Natural Language Processing (NLP). It not only establishes a benchmark but also introduces the NLPAD datasets—8 curated and transformed datasets derived from existing NLP classification datasets. These datasets are specifically tailored for NLP anomaly detection tasks and presented in a unified standard format to support and advance research in this domain.
To ensure a robust evaluation, NLP-ADBench includes results from 19 algorithms applied to the 8 NLPAD datasets, categorized into two groups:
- 3 end-to-end algorithms that directly process raw text data to produce anomaly detection outcomes.
- 16 embedding-based algorithms, created by applying 8 traditional anomaly detection methods to text embeddings generated using two models:
- BERT's `bert-base-uncased`(**BERT**)
- OpenAI’s `text-embedding-3-large`(**OpenAI**).
## NLPAD Datasets
The datasets required for this project can be downloaded from the following Hugging Face links:
1. **NLPAD Datasets**: These are the datasets mentioned in NLP-ADBench paper. You can download them from:
- [NLP-AD Datasets](https://huggingface.co/datasets/kendx/NLP-ADBench/tree/main/datasets)
2. **Pre-Extracted Embeddings**: For embedding-based algorithms, we have already extracted these embeddings. If you want to use them directly, you can download them from:
- [Pre-Extracted Embeddings](https://huggingface.co/datasets/kendx/NLP-ADBench/tree/main/embeddings)
## Sample Usage
### Environment Setup Instructions
Follow these steps to set up the development environment using the provided Conda environment file:
1. **Install Anaconda or Miniconda**:
Download and install Anaconda or Miniconda from [here](https://docs.conda.io/en/latest/miniconda.html).
2. **Create the Environment**:
Using the terminal, navigate to the directory containing the `environment.yml` file (found in the [GitHub repository](https://github.com/USC-FORTIS/NLP-ADBench)) and run:
```bash
conda env create -f environment.yml
```
3. **Activate the Environment**:
Activate the newly created environment using:
```bash
conda activate nlpad
```
### Import data
Get `Pre-Extracted Embeddings` data from the [huggingface link](https://huggingface.co/datasets/kendx/NLP-ADBench/tree/main/embeddings) and put it in the data folder.
Place all downloaded embeddings data into the `feature` folder in the `./benchmark` directory of this project.
### Run the code
Run the following commands from the `./benchmark` directory of the project (after cloning the [GitHub repository](https://github.com/USC-FORTIS/NLP-ADBench)):
#### BERT
If you want to run a benchmark using data embedded with BERT's `bert-base-uncased` model, use this command:
````bash
python [algorithm_name]_benchmark.py bert
````
#### OpenAI
If you want to run a benchmark using data embedded with OpenAI's `text-embedding-3-large` model, use this command:
````bash
python [algorithm_name]_benchmark.py gpt
````
## Citation
If you find this work useful, please cite our paper:
```bibtex
@article{li2025nlp,
title={Nlp-adbench: Nlp anomaly detection benchmark},
author={Li, Yuangang and Li, Jiaqi and Xiao, Zhuo and Yang, Tiankai and Nian, Yi and Hu, Xiyang and Zhao, Yue},
journal={Findings of the Association for Computational Linguistics: EMNLP 2025},
year={2025}
}
``` |