---
license: mit
tags:
- pytorch
- text-classification
- emotion-detection
- mlp
- nlp
datasets:
- nelgiriyewithana/emotions
metrics:
- accuracy
- f1
---

# Emotions Classifier - Deep MLP

Model klasyfikacji emocji w tekście oparty na wielowarstwowym perceptronie (MLP) z regularyzacją.

## Model Details

- **Architektura:** Deep MLP (5 warstw ukrytych)
- **Input:** TF-IDF vectors (5000 features)
- **Output:** 6 klas emocji (sadness, joy, love, anger, fear, surprise)
- **Accuracy:** 88.69% na zbiorze testowym
- **Overfitting Gap:** 6.02%

### Architektura:
```
Input (5000) 
  → Linear(1024) + BatchNorm + ReLU + Dropout(0.5)
  → Linear(512) + BatchNorm + ReLU + Dropout(0.4)
  → Linear(256) + BatchNorm + ReLU + Dropout(0.3)
  → Linear(128) + BatchNorm + ReLU + Dropout(0.2)
  → Linear(6)
```

### Techniki regularyzacji:
- **Progresywny Dropout:** 0.5 → 0.4 → 0.3 → 0.2
- **Batch Normalization** po każdej warstwie
- **L2 Regularization** (weight decay = 1e-4)

## Usage

```python
import torch
import pickle
from sklearn.feature_extraction.text import TfidfVectorizer

# Wczytaj model
model = DeepMLP()
model.load_state_dict(torch.load("model.pth", map_location="cpu"))
model.eval()

# Wczytaj vectorizer i emotion_map
with open("vectorizer.pkl", "rb") as f:
    vectorizer = pickle.load(f)
with open("emotion_map.pkl", "rb") as f:
    emotion_map = pickle.load(f)

# Klasyfikacja
text = "I am so happy today!"
X = vectorizer.transform([text]).toarray()
X_tensor = torch.FloatTensor(X)

with torch.no_grad():
    outputs = model(X_tensor)
    _, predicted = torch.max(outputs, 1)
    emotion = emotion_map[predicted.item()]

print(f"Emotion: {emotion}")
```

## Training Details

- **Dataset:** Emotions Dataset (Kaggle) - 150,000 examples
- **Train/Val/Test split:** 70% / 15% / 15%
- **Optimizer:** Adam (lr=0.001, weight_decay=1e-4)
- **Batch size:** 256
- **Epochs:** 10
- **Loss function:** CrossEntropyLoss

## Performance

| Emotion | Precision | Recall | F1-Score |
|---------|-----------|--------|----------|
| sadness | 0.95 | 0.90 | 0.92 |
| joy | 0.89 | 0.93 | 0.91 |
| love | 0.81 | 0.73 | 0.77 |
| anger | 0.88 | 0.91 | 0.89 |
| fear | 0.83 | 0.86 | 0.84 |
| surprise | 0.72 | 0.77 | 0.74 |
| **Macro avg** | **0.85** | **0.85** | **0.85** |

## Citation

```bibtex
@misc{emotions-classifier-mlp,
  author = {Hubert Brzozowski},
  title = {Emotions Classifier - Deep MLP},
  year = {2026},
  publisher = {Hugging Face}
}
```

## License

MIT License