# Hate Speech Detection — Multilingual Sequential Transfer Learning
### GloVe Embeddings + Bidirectional LSTM (BiLSTM)

---

## What is this project about?

This project builds a system that can automatically detect **hate speech** in text written in three languages:
- **English** — standard English text
- **Hindi** — Hindi text (transliterated or native script)
- **Hinglish** — a mix of Hindi and English (very common in Indian social media)

The core question we are trying to answer is:

> **Does the order in which you teach a model different languages matter for how well it performs?**

For example — is a model that learns English first, then Hindi, then Hinglish better or worse than one that learns Hinglish first?

---

## The Dataset

| Property | Value |
|---|---|
| Total samples | 29,505 |
| English samples | 14,994 (50.8%) |
| Hindi samples | 9,738 (33.0%) |
| Hinglish samples | 4,774 (16.2%) |
| Hate speech (label=1) | 13,707 (46.5%) |
| Non-hate speech (label=0) | 15,799 (53.5%) |

![Language Distribution](output/figures/language_distribution.png)

The dataset was split into three parts:
- **Training set** — 17,704 samples (used to teach the model)
- **Validation set** — 2,950 samples (used to monitor learning during training)
- **Test set** — 8,852 samples (used only at the end to measure real performance)

---

## The Model — What is GloVe + BiLSTM?

Think of the model like a two-part reading machine:

### Part 1: GloVe Embeddings (the dictionary)
Before the model can understand words, it needs to know what words *mean* relative to each other. GloVe (Global Vectors) is a pre-trained lookup table of **300,000+ English words**, where each word is represented as a list of 300 numbers that capture its meaning. Words with similar meanings end up with similar numbers.

- We used `glove.6B.300d.txt` — 6 billion word training corpus, 300 dimensions
- The embedding layer is **frozen** (not updated during training) — we keep GloVe's knowledge as-is and only train the layers on top

### Part 2: Bidirectional LSTM (the reader)
An LSTM (Long Short-Term Memory) is a type of neural network designed to read sequences — like sentences — and remember what it read. **Bidirectional** means it reads the sentence both forwards and backwards, so it understands context from both directions.

```
Input sentence
     ↓
GloVe Embeddings (300d, frozen)
     ↓
BiLSTM (128 units, reads left→right AND right←left)
     ↓
Dropout (50% — randomly switches off neurons to prevent overfitting)
     ↓
Dense layer (64 neurons, ReLU activation)
     ↓
Output (1 neuron, Sigmoid — gives a probability 0 to 1)
     ↓
> 0.5 = Hate Speech, ≤ 0.5 = Not Hate Speech
```

---

## The Training Strategy — What is Transfer Learning?

**Transfer learning** means the model carries what it learned from one task into the next. Like a student who already knows French — learning Spanish is easier because both share Latin roots.

In our case, we train the model on one language, and instead of starting fresh for the next language, we **keep all the weights (knowledge)** from the previous training. The model continues learning from where it left off.

### The Bug We Fixed
The original code was creating a **brand new model** for every language — resetting all the weights each time. That is not transfer learning, it's just training three separate models. We fixed this by building the model **once** and sequentially fine-tuning it.

```python
# WRONG — model reset every loop iteration
for lang in languages:
    model = Sequential()   # ← new model = no transfer learning
    model.fit(...)

# CORRECT — model built once, weights carry forward
model = build_model()      # ← built once
for lang in languages:
    model.fit(...)         # ← continues learning from previous language
```

---

## Plan B — The Experiment

We ran all **6 possible orderings** of the three languages, each followed by a final training round on the complete shuffled dataset:

| # | Strategy |
|---|---|
| 1 | English → Hindi → Hinglish → Full |
| 2 | English → Hinglish → Hindi → Full |
| 3 | Hindi → English → Hinglish → Full |
| 4 | Hindi → Hinglish → English → Full |
| 5 | Hinglish → English → Hindi → Full |
| 6 | Hinglish → Hindi → English → Full |

For each strategy, training happens in 4 phases. **After each phase**, we immediately evaluate the model on that specific language's test data and record all metrics. This tells us how well the model performs at each stage of the learning journey.

```
Phase 1: Train on Language A  →  Test on Language A test set  →  Record metrics + plots
Phase 2: Train on Language B  →  Test on Language B test set  →  Record metrics + plots
Phase 3: Train on Language C  →  Test on Language C test set  →  Record metrics + plots
Phase 4: Train on Full data   →  Test on Full test set        →  Record metrics + plots
```

Each phase used **8 epochs** with batch size 32 (64 for the full phase).

---

## Metrics — What do we measure?

| Metric | What it means in plain English |
|---|---|
| **Accuracy** | Out of all predictions, how many were correct? |
| **Balanced Accuracy** | Accuracy adjusted for class imbalance (more fair) |
| **Precision** | Of everything the model flagged as hate speech, how much actually was? |
| **Recall** | Of all actual hate speech, how much did the model catch? |
| **Specificity** | Of all non-hate speech, how much did the model correctly ignore? |
| **F1 Score** | Balance between Precision and Recall (harmonic mean) |
| **ROC-AUC** | Overall ability to distinguish hate from non-hate (1.0 = perfect) |

---

## Results Summary

Full results are in `output/results_tables/all_strategies_results.csv`. Key highlights:

### English phase performance across strategies (best language)

| Strategy | Accuracy | F1 | ROC-AUC |
|---|---|---|---|
| English → Hindi → Hinglish → Full | 0.7701 | 0.7696 | 0.8504 |
| English → Hinglish → Hindi → Full | 0.7721 | 0.7743 | 0.8525 |
| Hindi → English → Hinglish → Full | 0.7780 | 0.7830 | 0.8549 |
| Hindi → Hinglish → English → Full | 0.7780 | 0.7816 | 0.8563 |
| Hinglish → English → Hindi → Full | 0.7716 | 0.7829 | 0.8484 |
| Hinglish → Hindi → English → Full | 0.7765 | 0.7811 | 0.8534 |

### Full dataset phase (final performance)

| Strategy | Accuracy | F1 | ROC-AUC |
|---|---|---|---|
| English → Hindi → Hinglish → Full | 0.6796 | 0.5923 | 0.7599 |
| English → Hinglish → Hindi → Full | 0.6813 | 0.6244 | 0.7535 |
| Hindi → English → Hinglish → Full | 0.6854 | 0.6419 | 0.7528 |
| Hindi → Hinglish → English → Full | 0.6865 | 0.6364 | 0.7507 |
| Hinglish → English → Hindi → Full | 0.6778 | 0.6285 | 0.7521 |
| Hinglish → Hindi → English → Full | 0.6845 | 0.6301 | 0.7548 |

### Key observations
- **English** consistently achieves the highest accuracy (~77%) regardless of when it is trained — likely because GloVe embeddings are English-centric
- **Hindi** is the hardest language — accuracy hovers around 55–59% across all strategies
- **Hinglish** sits in the middle (~66–70%) which makes sense as it borrows heavily from English
- Strategies that train **Hindi first** (`Hindi → English → Hinglish`) tend to recover better in later phases, suggesting the model benefits from tackling the hardest language early
- The **Full phase** shows consistent ~68% accuracy across all strategies, suggesting the final shuffled training normalises the differences introduced by ordering

---

## Plots by Strategy

### Strategy 1: English → Hindi → Hinglish → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| English | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[english]_curves.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[english]_cm.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[english]_roc.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[english]_pr.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[english]_f1.png) |
| Hindi | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hindi]_curves.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hindi]_cm.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hindi]_roc.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hindi]_pr.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hindi]_f1.png) |
| Hinglish | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hinglish]_curves.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hinglish]_cm.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hinglish]_roc.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hinglish]_pr.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[hinglish]_f1.png) |
| Full | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[Full]_curves.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[Full]_cm.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[Full]_roc.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[Full]_pr.png) | ![](output/figures/english_to_hindi_to_hinglish/english_to_hindi_to_hinglish_[Full]_f1.png) |

---

### Strategy 2: English → Hinglish → Hindi → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| English | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[english]_curves.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[english]_cm.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[english]_roc.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[english]_pr.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[english]_f1.png) |
| Hinglish | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hinglish]_curves.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hinglish]_cm.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hinglish]_roc.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hinglish]_pr.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hinglish]_f1.png) |
| Hindi | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hindi]_curves.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hindi]_cm.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hindi]_roc.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hindi]_pr.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[hindi]_f1.png) |
| Full | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[Full]_curves.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[Full]_cm.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[Full]_roc.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[Full]_pr.png) | ![](output/figures/english_to_hinglish_to_hindi/english_to_hinglish_to_hindi_[Full]_f1.png) |

---

### Strategy 3: Hindi → English → Hinglish → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| Hindi | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hindi]_curves.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hindi]_cm.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hindi]_roc.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hindi]_pr.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hindi]_f1.png) |
| English | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[english]_curves.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[english]_cm.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[english]_roc.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[english]_pr.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[english]_f1.png) |
| Hinglish | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hinglish]_curves.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hinglish]_cm.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hinglish]_roc.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hinglish]_pr.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[hinglish]_f1.png) |
| Full | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[Full]_curves.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[Full]_cm.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[Full]_roc.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[Full]_pr.png) | ![](output/figures/hindi_to_english_to_hinglish/hindi_to_english_to_hinglish_[Full]_f1.png) |

---

### Strategy 4: Hindi → Hinglish → English → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| Hindi | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hindi]_curves.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hindi]_cm.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hindi]_roc.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hindi]_pr.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hindi]_f1.png) |
| Hinglish | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hinglish]_curves.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hinglish]_cm.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hinglish]_roc.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hinglish]_pr.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[hinglish]_f1.png) |
| English | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[english]_curves.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[english]_cm.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[english]_roc.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[english]_pr.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[english]_f1.png) |
| Full | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[Full]_curves.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[Full]_cm.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[Full]_roc.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[Full]_pr.png) | ![](output/figures/hindi_to_hinglish_to_english/hindi_to_hinglish_to_english_[Full]_f1.png) |

---

### Strategy 5: Hinglish → English → Hindi → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| Hinglish | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hinglish]_curves.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hinglish]_cm.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hinglish]_roc.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hinglish]_pr.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hinglish]_f1.png) |
| English | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[english]_curves.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[english]_cm.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[english]_roc.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[english]_pr.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[english]_f1.png) |
| Hindi | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hindi]_curves.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hindi]_cm.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hindi]_roc.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hindi]_pr.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[hindi]_f1.png) |
| Full | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[Full]_curves.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[Full]_cm.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[Full]_roc.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[Full]_pr.png) | ![](output/figures/hinglish_to_english_to_hindi/hinglish_to_english_to_hindi_[Full]_f1.png) |

---

### Strategy 6: Hinglish → Hindi → English → Full

| Phase | Training Curves | Confusion Matrix | ROC Curve | PR Curve | F1 Curve |
|---|---|---|---|---|---|
| Hinglish | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hinglish]_curves.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hinglish]_cm.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hinglish]_roc.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hinglish]_pr.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hinglish]_f1.png) |
| Hindi | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hindi]_curves.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hindi]_cm.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hindi]_roc.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hindi]_pr.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[hindi]_f1.png) |
| English | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[english]_curves.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[english]_cm.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[english]_roc.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[english]_pr.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[english]_f1.png) |
| Full | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[Full]_curves.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[Full]_cm.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[Full]_roc.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[Full]_pr.png) | ![](output/figures/hinglish_to_hindi_to_english/hinglish_to_hindi_to_english_[Full]_f1.png) |

---

## Output Files

```
output/
├── dataset_splits/
│   ├── train.csv                          # 17,704 training samples
│   ├── val.csv                            # 2,950 validation samples
│   └── test.csv                           # 8,852 test samples
│
├── results_tables/
│   ├── all_strategies_results.csv         # All 24 rows (6 strategies × 4 phases)
│   ├── english_to_hindi_to_hinglish_results.csv
│   ├── english_to_hinglish_to_hindi_results.csv
│   ├── hindi_to_english_to_hinglish_results.csv
│   ├── hindi_to_hinglish_to_english_results.csv
│   ├── hinglish_to_english_to_hindi_results.csv
│   └── hinglish_to_hindi_to_english_results.csv
│
└── figures/
    ├── language_distribution.png          # Pie chart of dataset languages
    │
    ├── english_to_hindi_to_hinglish/      # One folder per strategy
    │   ├── *_[english]_curves.png         # Train/Val accuracy + loss
    │   ├── *_[english]_cm.png             # Confusion matrix
    │   ├── *_[english]_roc.png            # ROC curve
    │   ├── *_[english]_pr.png             # Precision-Recall curve
    │   ├── *_[english]_f1.png             # F1 vs Threshold curve
    │   ├── *_[hindi]_curves.png
    │   ├── *_[hindi]_cm.png  ...
    │   ├── *_[hinglish]_curves.png
    │   ├── *_[hinglish]_cm.png  ...
    │   ├── *_[Full]_curves.png
    │   └── *_[Full]_cm.png  ...
    │
    ├── english_to_hinglish_to_hindi/
    ├── hindi_to_english_to_hinglish/
    ├── hindi_to_hinglish_to_english/
    ├── hinglish_to_english_to_hindi/
    └── hinglish_to_hindi_to_english/
```

---

## How to Run

### Requirements
```bash
pip install tensorflow scikit-learn pandas seaborn matplotlib
```

You also need GloVe embeddings (`glove.6B.300d.txt`) placed at `/root/glove.6B.300d.txt`:
```bash
wget http://nlp.stanford.edu/data/glove.6B.zip && unzip glove.6B.zip
```

### Run
```bash
python main.py
```

Training was performed on an NVIDIA H200 GPU (Vast.ai) — total runtime approximately 15–20 minutes for all 6 strategies.

---

## Project Structure

```
SASC/
├── main.py          # Full training + evaluation pipeline
├── dataset.csv      # Raw dataset (29,505 samples)
├── README.md        # This file
└── output/          # All results, figures, and model checkpoints
```