Update README.md

README.md

@@ -1,199 +1,173 @@
 ---
+language:
+- en
+- bem
+- ny
+tags:
+- multi-task
+- sentiment-analysis
+- topic-classification
+- language-identification
+- multilingual
+- transformer
+- zambia
+- lusaka
+license: apache-2.0
 library_name: transformers
-tags: []
+pipeline_tag: text-classification
+model-index:
+- name: LusakaLang-MultiTask
+  results:
+  - task:
+      name: Language Identification
+      type: text-classification
+    dataset:
+      name: LusakaLang Language Data
+      type: lusakalang
+      split: test
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 0.97
+    - name: f1_macro
+      type: f1
+      value: 0.96
+
+  - task:
+      name: Sentiment Analysis
+      type: text-classification
+    dataset:
+      name: LusakaLang Sentiment Data
+      type: lusakalang
+      split: test
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 0.9322
+    - name: f1_macro
+      type: f1
+      value: 0.9216
+    - name: f1_negative
+      type: f1
+      value: 0.8649
+    - name: f1_neutral
+      type: f1
+      value: 0.95
+    - name: f1_positive
+      type: f1
+      value: 0.95
+
+  - task:
+      name: Topic Classification
+      type: text-classification
+    dataset:
+      name: LusakaLang Topic Data
+      type: lusakalang
+      split: test
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 0.91
+    - name: f1_macro
+      type: f1
+      value: 0.90
 ---
 
-# Model Card for Model ID
+# **LusakaLang Multi‑Task Model (Language + Sentiment + Topic)**
 
-<!-- Provide a quick summary of what the model is/does. -->
+## **Model Description**
 
+**LusakaLang‑MultiTask** is a unified transformer model built on top of **`bert-base-multilingual-cased`**, designed to perform **three tasks simultaneously**:
 
+1. **Language Identification**  
+2. **Sentiment Analysis**  
+3. **Topic Classification**
 
-## Model Details
+The model integrates three fine‑tuned LusakaLang checkpoints:
 
-### Model Description
+- `Kelvinmbewe/mbert_Lusaka_Language_Analysis`
+- `Kelvinmbewe/mbert_LusakaLang_Sentiment_Analysis`
+- `Kelvinmbewe/mbert_LusakaLang_Topic`
 
-<!-- Provide a longer summary of what this model is. -->
+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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
+---
 
-[More Information Needed]
+# **Why This Model Matters**
 
-#### Software
+Zambian communication is multilingual, fluid, and highly context‑dependent.  
+A single message may include:
 
-[More Information Needed]
+- English  
+- Bemba  
+- Nyanja  
+- Slang  
+- Code‑switching  
+- Cultural idioms  
+- Indirect emotional cues  
 
-## Citation [optional]
+This model is designed specifically for that environment.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+It excels at:
 
-**BibTeX:**
+- 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  
 
-[More Information Needed]
+---
 
-**APA:**
+# **Training Architecture**
 
-[More Information Needed]
+The model uses:
 
-## Glossary [optional]
+- **Shared Encoder:** mBERT  
+- **Head 1:** Language classifier  
+- **Head 2:** Sentiment classifier  
+- **Head 3:** Topic classifier  
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+This multi‑task setup improves generalization and reduces inference cost.
 
-[More Information Needed]
+---
 
-## More Information [optional]
+# **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 |
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+# **How to Use This Model**
 
-[More Information Needed]
+## **Load the Multi‑Task Model**
 
-## Model Card Contact
+```python
+from transformers import AutoTokenizer
+import torch
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("Kelvinmbewe/LusakaLang-MultiTask")
+model = torch.load("Kelvinmbewe/LusakaLang-MultiTask/model.pt")
+model.eval()