emotions-classifier-mlp

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

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

Citation

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

License

MIT License