Update README.md

README.md

@@ -39,17 +39,47 @@ Crucially, it is highly effective at isolating environmental topics from **broad
 >💡 Designed for: Precision text classification in sustainable finance, ESG analysis, and corporate governance contexts.
 
 ---
+## 🚀 Usage
 
-## 🚀 Uses
+### ⚡ Quick Start
 
-### ✅ Direct Use
+Install dependencies first:
+```bash
+pip install transformers torch
+```
 
-The model takes a structured text input describing a shareholder proposal and predicts whether it is conceptually focused on climate change or environmental sustainability.
+Then run the following:
+```python
+from transformers import pipeline
+
+# 1. Load the model
+classifier = pipeline(
+    "text-classification",
+    model="Jidi1997/ClimateBERT_GPROP_Detector"
+)
+
+# 2. Construct a proposal input
+test_proposal = """
+A(An) institutional-type sponsor has filed a shareholder proposal to a(an)
+energy-sector company. This proposal requests: the company to issue a report
+on its greenhouse gas emissions reduction targets.
+It falls under a broader agenda class that may include items not directly
+relevant to this specific proposal: Environmental/Social.
+"""
+
+# 3. Run inference
+result = classifier(test_proposal)
+print(result)
+# Expected output: [{'label': 'yes', 'score': 0.99...}]
+# label='yes' → Green proposal detected (Label 1)
+# label='no'  → Non-green proposal (Label 0)
+```
 
-**📌 Recommended Input Format**
+---
 
-To achieve optimal performance, input text can be organized as the structure of the training data:
+### 📌 Recommended Input Format
 
+To achieve optimal performance, structure your input text to mirror the training data format:
 ```
 "A(An) {sponsor_type}-type sponsor has filed a shareholder proposal to a(an)
 {sic2_des}-sector company. This proposal requests: {resolution}.
@@ -57,6 +87,16 @@ It falls under a broader agenda class that may include items not directly
 relevant to this specific proposal: {AgendaCodeInformation}"
 ```
 
+| Field | Description | Example |
+|:---|:---|:---|
+| `{sponsor_type}` | Type of proposal sponsor | `institutional`, `individual` |
+| `{sic2_des}` | SIC-2 industry sector description | `energy`, `manufacturing` |
+| `{resolution}` | Full text of the proposal resolution | *"the company to report on..."* |
+| `{AgendaCodeInformation}` | ISS agenda code label *(optional but recommended)* | `Environmental/Social` |
+
+> 💡 **Tip:** The `{AgendaCodeInformation}` field is optional but including it generally improves prediction confidence, as it provides additional categorical context into brief resolution context.
+
+
 
 ## 📦 Training Data