Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+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:** Apache 2.0
+- **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)