| --- |
| language: |
| - en |
| license: mit |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-classification |
| pretty_name: MCA^2 Data & Embeddings |
| tags: |
| - anomaly-detection |
| - multi-view |
| - embeddings |
| - representation-learning |
| - contrastive-learning |
| arxiv: 2601.17786 |
| dataset_info: |
| features: |
| - name: data |
| dtype: file |
| - name: embeddings |
| dtype: file |
| --- |
| |
| # MCA^2 Data & Embeddings |
|
|
| [**Paper**](https://huggingface.co/papers/2601.17786) | [**GitHub**](https://github.com/yankehan/MCA2) |
|
|
| This repository provides the **raw data (`data/`)** and the corresponding **precomputed multi-view embeddings (`embeddings/`)** for **MCA^2**, a two-stage multi-view text anomaly detection (TAD) framework. |
|
|
| MCA^2 exploits embeddings from multiple pretrained language models (views) and integrates them via a multi-view reconstruction model, contrastive collaboration, and adaptive allocation to identify anomalies. This dataset release facilitates reproduction by providing pre-extracted vectors, avoiding the need for expensive re-computation across various encoders (e.g., BERT, Stella, Qwen, and OpenAI). |
|
|
| ## Content |
|
|
| - **data/**: Dataset files including train/test splits (e.g., `.npz` and `.jsonl` files). |
| - **embeddings/**: Pre-extracted vectors grouped by dataset and split. Multiple embedding files correspond to different "views" or encoders. |
|
|
| ## Sample Usage |
|
|
| To reproduce the results for a specific dataset (such as OLID) using the MCA^2 framework, you can follow the instructions from the official repository: |
|
|
| ```bash |
| # 1. Setup environment |
| conda create -n MCA2 python=3.9 |
| conda activate MCA2 |
| pip install torch sentence-transformers numpy transformers scikit-learn pandas tqdm pyod accelerate |
| |
| # 2. Clone the repository and navigate to the evaluation directory |
| git clone https://github.com/yankehan/MCA2 |
| cd MCA2/multiview_two_stage/eval |
| |
| # 3. Run the evaluation script (ensure data and embeddings are placed in the project directory) |
| python ourmethod_eval.py --dataset olid --seeds 41,42,43,44,45 |
| ``` |
|
|
| ## Notes |
|
|
| - Embeddings can be large; it is recommended to start with a smaller dataset like **TAD-OLID** first. |
| - If downloads are slow, you may try using a Hugging Face mirror (e.g., `https://hf-mirror.com`). |
|
|
| ## Citation |
|
|
| If you use this dataset or the MCA^2 framework in your research, please cite: |
|
|
| ```bibtex |
| @article{liu2026beyond, |
| title={Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations}, |
| author={Yixin Liu, Kehan Yan, Shiyuan Li and others}, |
| journal={arXiv preprint arXiv:2601.17786}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the [MIT License](https://opensource.org/licenses/MIT). |
