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README.md

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+---
+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