Improve model card and add metadata

This PR improves the model card for the POST model.

Summary of changes:
- Added `pipeline_tag: other` to the metadata.
- Included links to the paper and official GitHub repository.
- Added key highlights from the paper.
- Included sample usage (evaluation scripts) directly from the GitHub README.
- Updated the citation section with the provided BibTeX.

README.md

@@ -1,10 +1,47 @@
 ---
 license: apache-2.0
+pipeline_tag: other
 ---
 
-Pretrained POST models, please consider citing our work:
+# POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
 
+This repository contains the official PyTorch implementation and pre-trained checkpoints for the paper [POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection](https://huggingface.co/papers/2605.18128).
+
+## 💡 Highlights
+ - **Novel Framework**: We propose POST, a model that unifies spatial and temporal anomaly modeling under a joint prior–observation adversarial learning framework. 
+ - **Anomaly Localization**: The adversarial optimization not only improves time-wise detection but also enables the model to localize anomalies to specific channels.
+ - **SMD+ Dataset**: Construction of a dataset featuring precise channel-wise annotations, serving as a generic testbed for anomaly localization in MTS. 
+ - **SOTA Performance**: Extensive experiments demonstrate that POST consistently outperforms state-of-the-art (SOTA) baselines in standard anomaly detection tasks.
+
+## 🛠️ Resources
+- **Code**: [GitHub Repository](https://github.com/anocodetest1/POST)
+- **Paper**: [huggingface.co/papers/2605.18128](https://huggingface.co/papers/2605.18128)
+
+## 🚀 Get Started
+
+### Evaluation
+To evaluate the trained models on the benchmarks, run the following scripts. Ensure you have the datasets prepared and the pre-trained weights placed into the `./checkpoints/` folder (named as `<dataset_name>_checkpoint.pth`).
+
+```bash
+# SMD
+python main.py --anomaly_ratio 0.5 --batch_size 64 --mode test --dataset SMD --data_path dataset/SMD --input_c 38 --output_c 38
+# MSL
+python main.py --anomaly_ratio 1. --batch_size 64 --mode test --dataset MSL --data_path dataset/MSL --input_c 55 --output_c 55
+# SMAP
+python main.py --anomaly_ratio 1. --batch_size 64 --mode test --dataset SMAP --data_path dataset/SMAP --input_c 25 --output_c 25
+# SWaT
+python main.py --anomaly_ratio 1. --win_size 150 --batch_size 64 --mode test --dataset SWaT --data_path dataset/SWaT --input_c 51 --output_c 51
+# PSM
+python main.py --anomaly_ratio 1. --batch_size 64 --mode test --dataset PSM --data_path dataset/PSM --input_c 25 --output_c 25
+# SMD+
+python main_channel.py --anomaly_ratio 0.5 --batch_size 64 --mode test --dataset SMD+ --data_path dataset/SMD+ --input_c 38 --output_c 38
 ```
+
+## Suggested citation
+
+Please consider citing our work:
+
+```bibtex
 @misc{zhang2026postpriorobservationadversariallearning,
       title={POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection}, 
       author={Suofei Zhang and Yaxuan Zheng and Haifeng Hu},