Model Card for Retail Product Title Classifier (E5 fine-tuned)
Model Details
Model Description
A fine-tuned version of intfloat/multilingual-e5-base, adapted for the classification of retail product titles in Ukrainian and English.
The model is optimized for noisy, real-world data (e.g., typos, abbreviations) typically encountered in e-commerce catalogues.
Developed by: Viacheslav Trachov
Model type: Transformer Encoder (E5)
Language(s): Ukrainian, English
License: MIT
Finetuned from model: intfloat/multilingual-e5-base
Uses
Direct Use
Classifying short, noisy product titles into predefined retail categories.
Designed for retail inventory management, e-commerce catalogues, and internal search optimization.
Out-of-Scope Use
Free-text generation or long-form document classification.
Tasks requiring high performance on languages other than Ukrainian/English.
Bias, Risks, and Limitations
Performance may degrade on titles that mix multiple languages or are heavily abbreviated beyond retail-specific contexts.
Categories must match the domain and fine-tuning setup (i.e., Ukrainian e-commerce retail).
Recommendations
Use confidence thresholds to route low-confidence predictions for manual review if critical.
Test on domain-specific datasets if adapting to new industries.
Training Details
Training Data
~60,000 real-world Ukrainian product titles from an e-commerce aggregator.
Titles were preprocessed minimally (lowercasing, space normalization).
Additional synthetic examples were generated for underrepresented categories using ChatGPT-4.
Training Procedure
Finetuned for multi-class classification using Cross-Entropy Loss.
Max sequence length: 48 tokens
Learning rate: 5e-5
Batch size: 64
Epochs: 15
Hardware: NVIDIA V100 GPU
Evaluation
Macro F1-score used due to class imbalance.
Results:
macro-F1 (for clean data) 0.830
macro-F1 (for noisy data) 0.777
Model achieved strong robustness under simulated typographical noise (~6.3% macro-F1 degradation)
