Update README.md

README.md

@@ -24,7 +24,7 @@ metrics:
   <img src="https://img.shields.io/badge/Domain-ESG%20%7C%20Climate%20Finance-teal?style=for-the-badge&logo=leaflet&logoColor=white" alt="Domain"/>
 </p>
 
-*A fine-tuned BERT-based language model for classifying environmental and climate-related shareholder proposals with high precision.*
+*A fine-tuned BERT-based language model to detect green shareholder proposals with high precision.*
 
 </div>
 
@@ -32,29 +32,11 @@ metrics:
 
 ## 📋 Model Summary
 
-This model is a fine-tuned version of [`climatebert/distilroberta-base-climate-detector`](https://huggingface.co/climatebert/distilroberta-base-climate-detector), specifically designed to classify **shareholder proposals** into binary categories: **green** (climate/environmental) or **non-green**.
+This model is a fine-tuned version of climatebert/distilroberta-base-climate-detector. It is specifically engineered to classify shareholder proposals into green (climate/environmental) or non-green categories.
 
-It was trained on a highly curated dataset of Institutional Shareholder Services (ISS) proposals, achieving an **F1 score of 0.981** on the validation set.
+Crucially, it is highly effective at isolating environmental topics from **broad, mixed-ESG contexts** without being distracted by generic sustainability or governance buzzwords (etc. Neutrality, Waste, Water...). Trained on a meticulously curated dataset, it achieves an F1 score of 0.981 on the validation set.
 
-> 💡 **Designed for researchers and practitioners** in sustainable finance, ESG analysis, and corporate governance.
-
----
-
-## 🔍 Model Details
-
-| Property | Value |
-|:---|:---|
-| 🧠 **Base Model** | `climatebert/distilroberta-base-climate-detector` |
-| 🎯 **Task** | Binary Sequence Classification |
-| 🌐 **Language** | English |
-| 📄 **License** | Apache 2.0 *(model weights)* |
-
-### 🏷️ Label Schema
-
-| Label | Description |
-|:---:|:---|
-| `1` | ✅ Green / Climate-related proposal |
-| `0` | ❌ Non-green proposal |
+>💡 Designed for: Precision text classification in sustainable finance, ESG analysis, and corporate governance contexts.
 
 ---
 
@@ -66,28 +48,19 @@ The model takes a structured text input describing a shareholder proposal and pr
 
 **📌 Recommended Input Format**
 
-To achieve optimal performance, input text should mirror the structure of the training data:
+To achieve optimal performance, input text can be organized as the structure of the training data:
 
 ```
 "A(An) {sponsor_type}-type sponsor has filed a shareholder proposal to a(an)
 {sic2_des}-sector company. This proposal requests: {resolution}.
-[It falls under a broader agenda class that may include items not directly
-relevant to this specific proposal: {AgendaCodeInformation}]"
+It falls under a broader agenda class that may include items not directly
+relevant to this specific proposal: {AgendaCodeInformation}"
 ```
 
-### ⚠️ Out-of-Scope Use
-
-The following use cases are **not recommended**:
-
-- 🚫 Applying the model to **non-English** texts
-- 🚫 Using the model for **automated legal or compliance decision-making** without human oversight
-- 🚫 Generalizing to **broad ESG topics** outside of strict environmental/climate scopes *(e.g., social or governance issues like gender equality or animal welfare are explicitly trained as negative classes)*
-
----
 
 ## 📦 Training Data
 
-The model was fine-tuned on a custom **stratified dataset of 1,500 manually curated ISS shareholder proposals**. The dataset underwent rule-based correction to exclude tangentially environmental or purely social/governance proposals.
+The model was fine-tuned on a custom **stratified dataset of 1,500 manually curated ISS shareholder proposals**. The dataset underwent rule-based correction to exclude purely social/governance and blend proposals.
 
 📂 For full details on data sampling, text construction, and labeling rules, please refer to the **[gprop_training_dataset](https://huggingface.co/datasets/Jidi1997/gprop_training_dataset)**.
 
@@ -131,7 +104,7 @@ The model weights from **Epoch 8 (`checkpoint-600`)** were selected as the best
 
 ## 📚 Citation
 
-If you use this model in your research, please cite the associated working paper:
+If you use this model in your research, please cite the associated working paper: (Forthcoming)
 
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