Improve dataset card for NLP-ADBench: Add metadata, links, overview, sample usage, and citation

README.md

@@ -1,5 +1,94 @@
 ---
-license: mit
 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**).
+
+
+![Performance comparison of 19 Algorithms on 8 NLPAD datasets using AUROC](https://github.com/USC-FORTIS/NLP-ADBench/blob/main/figs/benchmark.png)
+
+## 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}
+}
+```