README.md

---
language:
  - ar
  - fr
license: mit
pipeline_tag: text-classification
tags:
  - misinformation-detection
  - fake-news
  - text-classification
  - algerian-darija
  - arabic
  - mbert
model_name: mBERT-Algerian-Darija
base_model: bert-base-multilingual-cased
---

# mBERT — Algerian Darija Misinformation Detection

Fine-tuned **BERT-base-multilingual-cased** for detecting misinformation in **Algerian Darija** text.

- **Base model**: `bert-base-multilingual-cased` (170M parameters)
- **Task**: Multi-class text classification (5 classes)
- **Classes**: F (Factual), R (Reporting), N (Non-factual), M (Misleading), S (Satire)

---

## Performance (Test set: 3,344 samples)

- **Accuracy**: 75.42%
- **Macro F1**: 64.48%
- **Weighted F1**: 75.70%

**Per-class F1**:
- Factual (F): 83.72%
- Reporting (R): 76.35%
- Non-factual (N): 81.01%
- Misleading (M): 61.46%
- Satire (S): 19.86%


---

## Training Summary

- **Max sequence length**: 128
- **Epochs**: 3 (early stopping)
- **Batch size**: 16
- **Learning rate**: 2e-5
- **Loss**: Weighted CrossEntropy
- **Seed**: 42 (reproducibility)

---

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

MODEL_ID = "Rahilgh/model4_1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device).eval()

LABEL_MAP = {0: "F", 1: "R", 2: "N", 3: "M", 4: "S"}
LABEL_NAMES = {
    "F": "Factual",
    "R": "Reporting",
    "N": "Non-factual",
    "M": "Misleading",
    "S": "Satire"
}

texts = [
    "قالك بلي رايحين ينحو الباك هذا العام",
    
]

for text in texts:
    inputs = tokenizer(
        text,
        return_tensors="pt",
        max_length=128,
        truncation=True,
        padding=True,
    ).to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)[0]
        pred_id = probs.argmax().item()
        confidence = probs[pred_id].item()

    label = LABEL_MAP[pred_id]
    print(f"Text: {text}")
    print(f"Prediction: {LABEL_NAMES[label]} ({label}) — {confidence:.2%}\n")