Update README.md

README.md

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