MTGTag

Magic: The Gathering Card Functional Tagging System | August 2025 - Present

Python PyTorch Transformers NLP

System_Overview // Description description

MTGTag is a machine learning pipeline that automatically classifies Magic: The Gathering cards into 81 functional labels. Using fine-tuned DistilBERT, the system achieves 0.91 F1 score on validation data with significant improvements from domain-specific adaptation.

This project demonstrates end-to-end machine learning: data preparation, model fine-tuning, GPU training, evaluation, and iteration.

Key Features / Capabilities star

Fine-Tuned DistilBERT

Leverages pre-trained DistilBERT for efficient transformer-based classification on Magic card text.

Domain Adaptation

Large performance gains from domain-specific adaptation on Magic card text and mechanics.

Multi-Label Classification

Handles 81 functional labels per card, capturing complex card mechanics and abilities.

GPU Training

Trained end-to-end on GPU in ~2 hours, demonstrating efficient deep learning workflows.

Rigorous Evaluation

Evaluated on holdout split (~10%) with threshold and metric iteration for optimal performance.

Production Ready

Saved model and inference pipeline for easy deployment and reuse.

Technical_Analysis // Deep_Dive analytics

Domain Adaptation

Standard NLP models struggle with Magic card text due to unique terminology and mechanics. The model was adapted using domain-specific training data, leading to large F1 improvements (0.91 final score).

Multi-Label Classification

Unlike binary or single-label tasks, this system predicts multiple labels per card. This required custom loss functions and threshold tuning to maximize F1 scores.

Efficient Training

DistilBERT is ~40% faster than BERT with minimal quality loss. Combined with GPU acceleration, training 33k+ cards takes just 2 hours.

Rigorous Evaluation

Used holdout validation split and iterated on thresholds/metrics. F1 score as primary metric ensures both precision and recall are optimized.

Knowledge_Log // Insights lightbulb

Transfer Learning: Fine-tuning pre-trained models for domain-specific tasks is far more efficient than training from scratch.
Multi-Label Classification: Handling multiple labels per sample requires different loss functions and evaluation metrics.
Domain Adaptation: NLP models need domain-specific data to perform well on specialized text (like Magic card mechanics).
GPU Training: Leveraging GPUs dramatically speeds up training; ~2 hours for 33k+ samples is feasible.
Evaluation & Iteration: Rigorous evaluation with holdout splits and metric tuning leads to robust models.

External_Links // Resources link

code View on GitHub