Kelvinmbewe
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mbert_Lusaka_Language_Analysis

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 library_name: transformers
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 ---
+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:
+      name: Sentiment Analysis
+      type: text-classification
+    dataset:
+      name: LusakaLang Test Set
+      type: lusakalang
+      config: default
+      split: test
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 0.9973
+    - name: precision
+      type: precision
+      value: 0.9973
+    - name: recall
+      type: recall
+      value: 0.9973
+    - name: f1
+      type: f1
+      value: 0.9978
 ---
+# **mbert LusakaLang Language Analysis Model**
+## Model Overview
+**LusakaLang** is a multilingual sentiment classification model fine-tuned from
+**`google-bert/bert-base-multilingual-cased (mBERT)`**.
+It is designed specifically for **Zambian language usage**, with a focus on:
+- **Zambian English (Lusaka variety)**
+- **Bemba**
+- **Nyanja (Chichewa)**
+The model captures **code-switching**, **local idioms**, **indirect expressions**, and **sarcasm** commonly used in everyday communication and social media in Zambia.
+---
+## Supported Languages
+- English (Zambian English)
+- Bemba
+- Nyanja (Chichewa)
+---
+## Task
+```
+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")
+```
+---
+## Training Data
+LusakaLang was fine-tuned using Zambia-focused multilingual datasets:
+- **English–Chichewa Sentence Pairs (MT560)**
+- **Code-170k-Bemba**
+- **BEMBA_big_c**
+These datasets enable strong performance on:
+- Informal and conversational text
+- Code-switched language
+- Culturally specific idioms and phrasing
+---
+## 📊 Evaluation Results (mBERT)
+The model was evaluated on the **test split** with multilingual data.
+### Overall Performance
+- **Test Loss:** 0.0039
+- **Accuracy:** **99.73%**
+- **Precision:** **99.73%**
+- **Recall:** **99.73%**
+- **Macro F1:** **99.78%**
+### Language-Specific F1 Scores
+- **Bemba:** **99.95%**
+- **Nyanja:** **99.69%**
+- **English:** **99.70%**
+### Training Configuration
+- **num_train_epochs:** 1
+- **learning_rate:** 2e-5
+- **batch_size:** 32
+---
+![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/7Z_jN0f6dWm6ZpOiK4Pyg.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/Jp8BsGpjsUG0XtIsDmKv8.png)
+Absolutely! Here's a **clear, easy-to-read section** you can add to your Hugging Face model card README to explain runtime expectations and GPU usage. You can paste it directly under your **Use Cases** or **How to Use This Model** sections.
+---
+### ⚡ Model Runtime and Performance Notes
+* **Model size:** mbert_LusakaLang is based on "google-bert/bert-base-multilingual-cased" (~178M parameters). Because of this, inference on large datasets can take longer than smaller models.
+* **Typical runtime:**
+  * **CPU:** 10–30 examples per second. Predicting thousands of examples may take 20–30+ minutes.
+  * **GPU:** 200–600 examples per second. Full test datasets usually process in under a minute.
+* **How to speed up inference:**
+  1. **Use a GPU:** Ensure PyTorch detects your GPU:
+     ```python
+     import torch
+     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+     model.to(device)
+     ```
+  2. **Increase batch size:** Adjust the evaluation batch size to make better use of GPU memory:
+     ```python
+     from transformers import TrainingArguments
+     eval_args = TrainingArguments(per_device_eval_batch_size=64)
+     ```
+  3. **Disable gradients during prediction:** This prevents unnecessary computation:
+     ```python
+     with torch.no_grad():
+         predictions = trainer.predict(dataset["test"])
+     ```
+  4. **Use mixed precision (fp16) on GPU:** Speeds up computation and reduces memory usage:
+     ```python
+     model.half()
+     ```
+* **Summary:** If you see long inference times (e.g., 30 minutes), it’s likely because predictions are running on CPU with small batch sizes. Using a GPU with optimized batch size drastically reduces runtime.
+---
+![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/Tv7qGipZ_ZRlvj5rIZI1a.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/xkqmOYLm2f044Hx1QOsnB.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/674ed988f86d2ca07fa23abe/4ZTEZC0f8iXmFx_dZhadX.png)