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emotions-classifier-mlp

Text Classification
PyTorch
emotion-detection
mlp
nlp
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emotions-classifier-mlp
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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