| --- |
| license: mit |
| language: |
| - uk |
| - en |
| base_model: |
| - intfloat/multilingual-e5-base |
| --- |
| # 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) |
