Link paper and GitHub repository, and add sample usage

Hi! I'm Niels from the Hugging Face community science team.

This pull request improves the dataset card for the MCA^2 dataset. Key changes include:
- Added the `arxiv: 2601.17786` tag to the metadata to link the dataset to its corresponding research paper.
- Included links to the official paper and GitHub repository.
- Added a "Sample Usage" section with code snippets derived from the GitHub README to help users reproduce the results.
- Provided a BibTeX citation for the paper.

README.md

@@ -1,51 +1,76 @@
 ---
-license: mit
 language:
-  - en
-tags:
-  - anomaly-detection
-  - multi-view
-  - embeddings
-  - representation-learning
-  - contrastive-learning
-task_categories:
-  - text-classification
+- en
+license: mit
 size_categories:
-  - 1K<n<10K
-pretty_name: "MCA^2 Data & Embeddings"
+- 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"
+  - name: data
+    dtype: file
+  - name: embeddings
+    dtype: file
 ---
 
 # MCA^2 Data & Embeddings
 
-This repository provides the **raw data (`data/`)** and the corresponding **precomputed multi-view embeddings (`embeddings/`)** for **MCA^2**, a **two-stage multi-view anomaly detection** framework.
+[**Paper**](https://huggingface.co/papers/2601.17786) | [**GitHub**](https://github.com/yankehan/MCA2)
 
-MCA^2 is not an end-to-end pipeline: it first generates embeddings for the same sample from multiple "views" (e.g., different encoders / feature sources) and stores them offline, then trains/evaluates the anomaly detector on top of these embeddings. This dataset release is intended to make reproduction easier and avoid re-computing expensive embeddings (especially those requiring paid APIs or heavy inference).
+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 (e.g., train/test splits)
-- **embeddings/**: pre-extracted vectors grouped by dataset and split (train/test); multiple embedding files correspond to different views/encoders
+- **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:
 
-## Usage
+```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
 
-1. Download the required dataset files under `data/`.
-2. Download the corresponding embedding files under `embeddings/`.
+# 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 first.
+- 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 in your work, please cite our MCA^2 paper.
+If you use this dataset or the MCA^2 framework in your research, please cite:
+
+```bibtex
+@article{yan2026beyond,
+  title={Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations},
+  author={Yan, Kehan and others},
+  journal={arXiv preprint arXiv:2601.17786},
+  year={2026}
+}
+```
 
 ## License
 
-MIT License
+This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).