---
language:
- en
- ny
- bem
tags:
- sentiment-analysis
- multilingual
- transformer
- zambia
- lusaka
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model:
- google-bert/bert-base-multilingual-cased
datasets:
- michsethowusu/english-chichewa_sentence-pairs_mt560
- michsethowusu/Code-170k-bemba
- Beijuka/BEMBA_big_c
metrics:
- accuracy
- precision
- recall
- f1
- confusion_matrix
- validation_loss
model-index:
- name: LusakaLang
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: LusakaLang Test Set
      type: lusakalang
      config: default
      split: test
    metrics:
    - type: accuracy
      value: 0.9973
      name: accuracy
    - type: precision
      value: 0.9973
      name: precision
    - type: recall
      value: 0.9973
      name: recall
    - type: f1
      value: 0.9978
      name: f1
---


## **Lusaka Language Analysis Model**

The Lusaka Language Analysis is a multilingual sentiment classification model fine‑tuned from  `google-bert/bert-base-multilingual-cased (mBERT)`.
and it is built specifically for Zambian linguistic contexts with a focus on:
- Zambian English (Lusaka variety)  
- Bemba  
- Nyanja (Chichewa) 

The model is optimized to recognize mixed-language usage, local idioms, indirect expressions, and contextual sarcasm commonly found in everyday 
Zambian communication and social media discourse.

---

## Task
```python
def classify_text(text):
    """
    Run inference on a single text input using the fine‑tuned LusakaLang model.
    Returns the predicted label and confidence score.
    """
    result = classifier(text)[0]
    label = result["label"]
    score = round(result["score"], 4)
    return label, score
samples = [
    "Muli shani bane, nalishiba bwino.",
    "How are you doing today?",
    "Tili bwino, zikomo kwambiri."
]
for s in samples:
    label, score = classify_text(s)
    print(f"Text: {s}\nPrediction: {label} (confidence={score})\n")
```

## Sample Output

```python
Text: Muli shani bane, nalishiba bwino.
Prediction: Bemba (confidence=0.9821)

Text: How are you doing today?
Prediction: English (confidence=0.9954)

Text: Tili bwino, zikomo kwambiri.
Prediction: Nyanja (confidence=0.9736)
```
---

## Language Graph
![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/OTroxtjtYgvijaMcv4Tpn.png)
> Note: The unknown langauge here represents a Mixed language of English, Bemba and Nyanja of varying degrees e.g GPS yenze nama issues so it made me delay my journey kwati nibamudala.


## Classification Report
![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/v5eLxfxuKDJ7Sd8uX2P9s.png)

## Confusion Matrix
![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/mxnDjRmAX-XLHzMfcWnfr.png)

## Word Cloud
![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/J-atqadjfCh7xUKRSRSnL.png)