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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- product-classification |
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- transformers |
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- pytorch |
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- distilbert |
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datasets: |
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- lokeshparab/amazon-products-dataset |
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model-index: |
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- name: Product Classifier B2 |
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results: [] |
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--- |
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# Product Classifier B2 |
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Tento model slouží k predikci kategorií produktů na základě jejich názvu nebo popisu... |
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# 🏍️ Amazon Product Classifier (Balanced B2) |
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This is a fine-tuned DistilBERT model for **multi-class classification** of product titles into Amazon-like product categories. |
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The model is based on `distilbert-base-uncased` and was trained on a **balanced subset** of the Amazon Products dataset. |
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## 🧠 Model Architecture |
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- Base: `distilbert-base-uncased` (6-layer, 768 hidden size) |
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- Classification Head: 2 dense layers with dropout + ReLU |
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- Output: softmax over 19 product categories |
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## 📊 Training Data |
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The model was trained on a balanced subset (≈40k samples) of the [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset), which contains product titles and their corresponding categories. |
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Preprocessing included: |
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- Removing empty/missing titles |
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- Keeping top-level categories only |
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- Balancing the dataset to avoid category bias |
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## 🍿 Example Categories |
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- beauty & health |
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- home & kitchen |
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- tv, audio & cameras |
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- computers & accessories |
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- clothing & accessories |
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- appliances |
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- sports & fitness |
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- grocery & gourmet foods |
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- ... (total 19) |
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## 🧪 Example Usage (Python) |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="your-username/product-classifier-model-B2") |
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result = classifier("Smartwatch with heart rate monitor and GPS tracking") |
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print(result) |
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# [{'label': 'stores', 'score': 0.94}] |
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``` |
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## 🚀 Intended Use |
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The model is designed to help developers quickly classify product titles into e-commerce categories, useful for: |
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- Auto-tagging items in online stores |
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- Cleaning and organizing product catalogs |
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- Building recommendation engines (in combination with embeddings) |
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## 📌 Limitations |
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- English-only (trained on `distilbert-base-uncased`) |
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- May not perform well on very short or ambiguous product names |
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- Not suitable for legal/medical/financial applications |
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## 📄 License & Source |
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- Model: MIT License |
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- Training Data: [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset) on Kaggle |
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(check license and attribution requirements on Kaggle page) |
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