SciTensor/e5-base-title-classifier-ukr

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)