Update README.md

README.md

@@ -11,7 +11,17 @@ tags:
 
 ## 📖 Model Overview
 
-This repository contains the models described in the paper **["Tracking Life's Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis"](https://aclanthology.org/2025.acl-long.345/)** (ACL 2025).
+**What is PsyEvent?**
+
+**PsyEvent** is a specialized NLP tool designed to extract and analyze major life events from unstructured social media text. Unlike general sentiment analysis, it focuses on identifying specific, objective occurrences (e.g., career, health) that significantly impact mental health trajectories (see Figure 1).
+
+<!-- ![](./assets/example_post.jpg) -->
+<div align="center">
+    <img src="./assets/example_post.jpg" width="600" alt="Model Architecture" />
+    <em>Figure 1: User post example.</em>
+</div>
+
+This repository contains the **PsyEvent** models described in the paper **["Tracking Life's Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis"](https://aclanthology.org/2025.acl-long.345/)** (ACL 2025).
 
 The system consists of two distinct models housed in this repository:
 1.  **Life Events Detection (`LE_detection`)**: A multi-label classifier that identifies 12 categories of life events from social media posts.
@@ -142,10 +152,25 @@ LE detection model performance on each life event category:
 
 ## Data Availability & Privacy Statement
 
-This model was trained on a subset of the **[SMHD (Self-reported Mental Health Diagnoses)](https://aclanthology.org/C18-1126/)** dataset. 
+This model was trained on **PsyEvent**, a subset of the **[SMHD (Self-reported Mental Health Diagnoses)](https://aclanthology.org/C18-1126/)** dataset. 
 
 **Due to the strict Data Usage Agreement of SMHD, we are prohibited from publishing or sharing any proportion of the original dataset (including our annotated subset).** Researchers interested in reproducing this work or using the data must apply for access directly from the original creators of [SMHD (Cohan et al., 2018)](https://ir.cs.georgetown.edu/resources/). We only provide the model weights and inference code here.
 
+
+## ⚠️ Ethical Considerations & Limitations
+
+**1. No Clinical Diagnosis:**
+This model is designed for **research purposes only**. It is not a clinical diagnostic tool and should not be used as a substitute for professional medical advice, diagnosis, or treatment.
+
+**2. No Automated Decision Making:**
+The model must **not** be used for automated decision-making in high-stakes scenarios, including but not limited to:
+  - Employment screening or hiring decisions.
+  - Insurance eligibility or claims processing.
+  - Legal assessment or administrative decision-making regarding individuals.
+
+**3. Bias & Errors:**
+Like all models trained on social media data, this model may reflect biases present in the training corpus. It may generate false positives or misinterpret metaphorical language. Users should critically evaluate the model's outputs.
+
 ### Citation
 If you use this model or dataset, please cite our paper:
 ```bibtex