metadata
license: mit
tags:
- emotion-classification
- multilabel-classification
- text-classification
- pytorch
- transformers
- distilbert
language: en
metrics:
- f1_macro
- f1_micro
- hamming_loss
- jaccard_score
model-index:
- name: emotion-multilabel-distilbert
results:
- task:
type: text-classification
name: Emotion Multilabel Classification
dataset:
type: emotion-classification
name: Emotion Classification Dataset
metrics:
- type: f1_macro
value: 0.4214
name: Macro F1-Score (Kaggle Test)
- type: f1_macro
value: 0.4275
name: Macro F1-Score (Validation)
- type: f1_micro
value: 0.4226
name: Micro F1-Score
- type: hamming_loss
value: 0.1816
name: Hamming Loss
- type: jaccard_score
value: 0.2679
name: Jaccard Score
Emotion Multilabel Classification Model
🎭 A fine-tuned DistilBERT model for multilabel emotion classification on text data.
Model Description
This model is fine-tuned from distilbert-base-uncased for multilabel emotion classification. It can predict multiple emotions simultaneously from text input across 14 different emotion categories.
Performance
Kaggle Competition Results
- 🏆 Kaggle Test Score (Macro F1): 0.4214
- 📊 Validation Score (Macro F1): 0.4275
- 🎯 Generalization Gap: 0.0061 (excellent!)
Detailed Metrics
- Macro F1-Score: 0.4275 (validation) / 0.4214 (test)
- Micro F1-Score: 0.4226
- Hamming Loss: 0.1816
- Jaccard Score: 0.2679
Model Architecture
- Base Model: distilbert-base-uncased
- Parameters: 66,373,646 (~66M)
- Architecture: DistilBERT + Custom Classification Head
- Dropout: 0.3
- Loss Function: BCEWithLogitsLoss with class weights
Emotions Supported
The model can predict the following 14 emotions: ['amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness']
Usage
from transformers import AutoTokenizer, AutoModel
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/emotion-multilabel-distilbert")
model = AutoModel.from_pretrained("your-username/emotion-multilabel-distilbert")
# Example usage
text = "I'm so happy and excited about this project!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.sigmoid(outputs.logits)
# Get emotions above threshold (0.5)
emotions = []
for i, prob in enumerate(predictions[0]):
if prob > 0.5:
emotions.append(emotion_columns[i])
print(f"Predicted emotions: {', '.join(emotions)}")
Training Details
- Training Duration: ~13 minutes
- Hardware: Tesla T4 GPU
- Epochs: 3
- Batch Size: 16
- Learning Rate: 2e-5
- Max Sequence Length: 128
- Optimizer: AdamW
- Class Weights: Applied for imbalanced dataset
Dataset Statistics
- Training Samples: 37,164
- Validation Samples: 9,291
- Test Samples: 8,199
- Average Labels per Sample: 3.21
- Most Common Pattern: 2-4 emotions per text
Performance Analysis
Strengths
- ✅ Good generalization (small validation-test gap)
- ✅ Reasonable multilabel predictions (avg 3.21 labels)
- ✅ Fast inference (~9 iterations/second)
- ✅ Memory efficient (66M parameters)
Areas for Improvement
- 📈 Macro F1 could be improved with hyperparameter tuning
- 📈 Class imbalance handling could be optimized
- 📈 Ensemble methods could boost performance
License
This model is released under the MIT License.