logs/day2.md

# 📘 Day 2: Sentiment & Zero-Shot Smackdown – Arabic Edition

Today, I dove deeper into two powerful Hugging Face pipelines — **Sentiment Analysis** and **Zero-Shot Classification** — with a twist: I focused on Arabic and multilingual performance. The goal? To find models that _actually_ handle Arabic well, dialects and all. 🌍🔤

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## 🔍 Part 1: Sentiment Analysis Recap

Previously, I found that the default `pipeline("sentiment-analysis")` worked okay for English but... Arabic? Not so much. So today was all about discovering **a better Arabic sentiment model**.

### 🧪 Models Tested:

| Model                                                    | Result                                                     |
| -------------------------------------------------------- | ---------------------------------------------------------- |
| `default` (`pipeline("sentiment-analysis")`)             | 👍 Good on English  <br>👎 ~55% accurate on Arabic         |
| `Anwaarma/Improved-Arabert-twitter-sentiment-No-dropout` | 😐 Inaccurate, struggled with meaning                      |
| `Abdo36/Arabert-Sentiment-Analysis-ArSAS`                | 🫤 Slightly better than default, but dialect handling weak |
### 🥇 Winner: `CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment`

- ✅ **Accuracy**: 95–99% on clear Arabic sentences
    
- 💬 **Dialect-Friendly**: Correctly classified Egyptian slang like  
    `"الواد سواق التوك توك جارنا عسل"` → **Positive 97%**
    
- ⚠️ **Weakness**: Lower performance on English (61%) and French (50%)
    

### 🧠 Key Takeaways

- **Fine-tuned, language-specific models** seriously outperform the defaults.
    
- **Dialect support = must-have** for real-world, diverse data.
    

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## 🔍 Part 2: Zero-Shot Classification — Arabic & Multilingual Trials

Could zero-shot models understand Arabic prompts _and_ interpret labels in multiple languages? Let’s see how they fared in a multilingual arena! 🧪🌐

### 1️⃣ `morit/arabic_xlm_xnli`

- ❌ Inaccurate, even on Arabic-only prompts
    
- ❌ Misaligned labels and scores
    

### 2️⃣ ✅ `MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7`

|Scenario|Accuracy|Improvement|
|---|---|---|
|Arabic → Arabic labels|✅ 96.1%|+21%|
|Arabic → English labels|✅ 86%|+37%|
|English → English labels|✅ 84%|+9%|
|English → Arabic labels|✅ 84%|vs. ~30% default|
|Mixed labels (Arabic + English)|✅ 92%|RTL handled properly|

📌 **UI Smartness**:

- Keeps English labels left-aligned, Arabic right-aligned
    
- No visual bugs or mis-scored outputs from RTL quirks ✔️
    

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## 🧠 Final Summary for Day 2

- 🎯 **Model selection matters**: The right model can boost performance by **20–25%**!
    
- 🗣️ **Dialect support** is key — generic models don’t cut it in nuanced use cases.
    
- 🔁 **Language pairing (input ↔ label)** is critical for zero-shot reliability.
    
- 🧑‍💻 **Proper RTL handling** helps avoid UI headaches and scoring issues.
    

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## 💡 What’s Next?

- 🚀 Try out another Hugging Face pipeline: **translation** or **summarization** sound exciting!
    
- 📚 Keep expanding my **language-aware model notebook** — with more dialects, labels, and real-world tests.