| | ---
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| | license: mit
|
| | tags:
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| | - pytorch
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| | - text-classification
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| | - emotion-detection
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| | - mlp
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| | - nlp
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| | datasets:
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| | - nelgiriyewithana/emotions
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| | metrics:
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| | - accuracy
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| | - f1
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| | ---
|
| |
|
| | # Emotions Classifier - Deep MLP
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| |
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| | Model klasyfikacji emocji w tekście oparty na wielowarstwowym perceptronie (MLP) z regularyzacją.
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| |
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| | ## Model Details
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| |
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| | - **Architektura:** Deep MLP (5 warstw ukrytych)
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| | - **Input:** TF-IDF vectors (5000 features)
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| | - **Output:** 6 klas emocji (sadness, joy, love, anger, fear, surprise)
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| | - **Accuracy:** 88.69% na zbiorze testowym
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| | - **Overfitting Gap:** 6.02%
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| |
|
| | ### Architektura:
|
| | ```
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| | Input (5000)
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| | → Linear(1024) + BatchNorm + ReLU + Dropout(0.5)
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| | → Linear(512) + BatchNorm + ReLU + Dropout(0.4)
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| | → Linear(256) + BatchNorm + ReLU + Dropout(0.3)
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| | → Linear(128) + BatchNorm + ReLU + Dropout(0.2)
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| | → Linear(6)
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| | ```
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| |
|
| | ### Techniki regularyzacji:
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| | - **Progresywny Dropout:** 0.5 → 0.4 → 0.3 → 0.2
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| | - **Batch Normalization** po każdej warstwie
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| | - **L2 Regularization** (weight decay = 1e-4)
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| |
|
| | ## Usage
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| |
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| | ```python
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| | import torch
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| | import pickle
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| | from sklearn.feature_extraction.text import TfidfVectorizer
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| |
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| | # Wczytaj model
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| | model = DeepMLP()
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| | model.load_state_dict(torch.load("model.pth", map_location="cpu"))
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| | model.eval()
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| |
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| | # Wczytaj vectorizer i emotion_map
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| | with open("vectorizer.pkl", "rb") as f:
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| | vectorizer = pickle.load(f)
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| | with open("emotion_map.pkl", "rb") as f:
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| | emotion_map = pickle.load(f)
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| |
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| | # Klasyfikacja
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| | text = "I am so happy today!"
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| | X = vectorizer.transform([text]).toarray()
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| | X_tensor = torch.FloatTensor(X)
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| |
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| | with torch.no_grad():
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| | outputs = model(X_tensor)
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| | _, predicted = torch.max(outputs, 1)
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| | emotion = emotion_map[predicted.item()]
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| |
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| | print(f"Emotion: {emotion}")
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| | ```
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| |
|
| | ## Training Details
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| |
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| | - **Dataset:** Emotions Dataset (Kaggle) - 150,000 examples
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| | - **Train/Val/Test split:** 70% / 15% / 15%
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| | - **Optimizer:** Adam (lr=0.001, weight_decay=1e-4)
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| | - **Batch size:** 256
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| | - **Epochs:** 10
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| | - **Loss function:** CrossEntropyLoss
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| |
|
| | ## Performance
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| |
|
| | | Emotion | Precision | Recall | F1-Score |
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| | |---------|-----------|--------|----------|
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| | | sadness | 0.95 | 0.90 | 0.92 |
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| | | joy | 0.89 | 0.93 | 0.91 |
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| | | love | 0.81 | 0.73 | 0.77 |
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| | | anger | 0.88 | 0.91 | 0.89 |
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| | | fear | 0.83 | 0.86 | 0.84 |
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| | | surprise | 0.72 | 0.77 | 0.74 |
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| | | **Macro avg** | **0.85** | **0.85** | **0.85** |
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| |
|
| | ## Citation
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| |
|
| | ```bibtex
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| | @misc{emotions-classifier-mlp,
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| | author = {Hubert Brzozowski},
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| | title = {Emotions Classifier - Deep MLP},
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| | year = {2026},
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| | publisher = {Hugging Face}
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| | }
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| | ```
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| |
|
| | ## License
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| |
|
| | MIT License
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| | |
