Update README.md

README.md

@@ -55,53 +55,36 @@ model-index:
       name: f1_macro
 ---
 
+## **LusakaLang Multi‑Task Model (Language + Sentiment + Topic)**
 
-# **LusakaLang Multi‑Task Model (Language + Sentiment + Topic)**
+This model is a unified transformer architecture built on top of **`bert-base-multilingual-cased`**, designed to perform **three tasks simultaneously**:
 
-## **Model Description**
+1. **[Language Identification](guide://action?prefill=Tell%20me%20more%20about%3A%20Language%20Identification)**  
+2. **[Sentiment Analysis](guide://action?prefill=Tell%20me%20more%20about%3A%20Sentiment%20Analysis)**  
+3. **[Topic Classification](guide://action?prefill=Tell%20me%20more%20about%3A%20Topic%20Classification)**  
 
-**LusakaLang‑MultiTask** is a unified transformer model built on top of **`bert-base-multilingual-cased`**, designed to perform **three tasks simultaneously**:
+The system integrates three fine‑tuned LusakaLang checkpoints:
 
-1. **Language Identification**  
-2. **Sentiment Analysis**  
-3. **Topic Classification**
+- **[Kelvinmbewe/mbert_Lusaka_Language_Analysis](guide://action?prefill=Tell%20me%20more%20about%3A%20Kelvinmbewe%2Fmbert_Lusaka_Language_Analysis)**  
+- **[Kelvinmbewe/mbert_LusakaLang_Sentiment_Analysis](guide://action?prefill=Tell%20me%20more%20about%3A%20Kelvinmbewe%2Fmbert_LusakaLang_Sentiment_Analysis)**  
+- **[Kelvinmbewe/mbert_LusakaLang_Topic](guide://action?prefill=Tell%20me%20more%20about%3A%20Kelvinmbewe%2Fmbert_LusakaLang_Topic)**  
 
-The model integrates three fine‑tuned LusakaLang checkpoints:
-
-- `Kelvinmbewe/mbert_Lusaka_Language_Analysis`
-- `Kelvinmbewe/mbert_LusakaLang_Sentiment_Analysis`
-- `Kelvinmbewe/mbert_LusakaLang_Topic`
-
-All tasks share a **single mBERT encoder**, with **three independent classifier heads**.  
-This architecture improves efficiency, reduces memory footprint, and enables consistent predictions across tasks.
+All tasks share a single mBERT encoder, supported by three independent classifier heads. This architecture enhances computational efficiency, reduces memory overhead 
+and promotes consistent, harmonized predictions across all tasks.
 
 ---
 
-# **Why This Model Matters**
-
-Zambian communication is multilingual, fluid, and highly context‑dependent.  
-A single message may include:
+## **Why This Model Matters**
 
-- English  
-- Bemba  
-- Nyanja  
-- Slang  
-- Code‑switching  
-- Cultural idioms  
-- Indirect emotional cues  
+Zambian communication is inherently multilingual, fluid, and deeply shaped by context. A single message may blend English, Bemba, Nyanja, local slang, 
+and frequent code‑switching, often expressed through culturally grounded idioms and subtle emotional cues. This model is designed specifically for that
+environment, where meaning depends not only on the words used but on how languages interact within a single utterance.
 
-This model is designed specifically for that environment.
+It excels at identifying the dominant language or detecting when multiple languages are being used together, interpreting sentiment even when it 
+is conveyed indirectly or through culturally specific phrasing, and classifying text into practical topics such as driver behaviour, payment issues,
+app performance, customer support, and ride availability. By capturing these nuances, the model provides a more accurate and context‑aware 
+understanding of real Zambian communication.
 
-It excels at:
-
-- Identifying the **dominant language** or **code‑switching**
-- Detecting **sentiment polarity** in culturally nuanced text
-- Classifying **topics** such as:
-  - driver behaviour  
-  - payment issues  
-  - app performance  
-  - customer support  
-  - ride availability  
 
 ---
 
@@ -118,34 +101,9 @@ This multi‑task setup improves generalization and reduces inference cost.
 
 ---
 
-# **Performance Summary**
-
-## **Language Identification**
-| Metric | Score |
-|--------|--------|
-| Accuracy | 0.97 |
-| Macro‑F1 | 0.96 |
-
-## **Sentiment Analysis (Epoch 30 — Final Checkpoint)**
-| Metric | Score |
-|--------|--------|
-| Accuracy | 0.9322 |
-| Macro‑F1 | 0.9216 |
-| Negative F1 | 0.8649 |
-| Neutral F1 | 0.95 |
-| Positive F1 | 0.95 |
-
-## **Topic Classification**
-| Metric | Score |
-|--------|--------|
-| Accuracy | 0.91 |
-| Macro‑F1 | 0.90 |
 
----
+## **How to Use This Model**
 
-# **How to Use This Model**
-
-## **Load the Multi‑Task Model**
 
 ```python
 from transformers import AutoTokenizer
@@ -182,15 +140,6 @@ predict_topic([
 ```
 
 
-```python
-@model{LusakaLangMultiTask,
-  author = {Kelvin Mbewe},
-  title = {LusakaLang Multi-Task Model},
-  year = 2025,
-  url = {https://huggingface.co/Kelvinmbewe/LusakaLang-MultiTask}
-}
-```
-
 
 ```python