Update README.md

docs: adding README and some information about model

README.md

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 ---
 license: apache-2.0
+language:
+- ru
+metrics:
+- accuracy
+base_model:
+- ai-forever/ruRoberta-large
+pipeline_tag: text-classification
+tags:
+- reviews
+- e-commercy
+- foodtech
 ---
+# Food Delivery Feedback Multi-Label Classification Model
+
+This model was developed for multi-label classification of customer feedback in the food delivery domain. It can identify up to 50 different aspects/issues from user reviews and feedback messages.
+
+## Model Description
+
+The model is designed to analyze customer feedback and automatically categorize it into multiple relevant labels, helping businesses quickly identify and address various aspects of their food delivery service.
+
+### Key Features:
+- Multi-label classification across 50 distinct categories
+- Specialized for food delivery domain feedback
+- Handles various aspects including delivery speed, food quality, packaging, customer service, etc.
+- Supports Russian language feedback processing
+
+### Use Cases:
+- Automated feedback categorization
+- Customer satisfaction analysis
+- Service quality monitoring
+- Issue identification and prioritization
+- Operational improvement insights
+
+### Performance:
+- Trained on about 3000 customer feedback messages
+- Achieves **Full Match Accuracy** on test set
+- Optimized for real-world food delivery feedback scenarios
+
+## Evaluation Metric: Full Match Accuracy
+
+The model is evaluated using a strict full match accuracy metric that requires exact matching between predicted and target labels.
+
+### Metric Description:
+- Type: Exact Match / Perfect Match Accuracy
+- Range: 0 to 1 (or 0% to 100%)
+- Format: Labels are represented as space-separated indices (e.g., "0 1 10")
+
+### Example:
+```python
+# Correct prediction (100% accuracy):
+Target:     "0 1 10"
+Prediction: "0 1 10"
+
+# Incorrect predictions (0% accuracy):
+Target:     "0 1 10"
+Prediction: "0 1"      # Partial match is considered incorrect
+Target:     "0 1 10"
+Prediction: "0 1 10 2" # Extra label is considered incorrect
+```
+
+### Labels Include:
+- Delivery time issues
+- Food quality concerns
+- Order accuracy
+- Packaging condition
+- Courier behavior
+- App/website functionality
+- Payment issues
+
+## Usage
+
+The model can be used for:
+- Real-time feedback classification
+- Batch processing of historical feedback
+- Customer support automation
+- Service quality analytics
+
+## Training
+
+The model was trained as part of an NLP competition focused on improving customer feedback analysis in the food delivery industry. It uses state-of-the-art transformer architecture optimized for multi-label classification tasks.
+
+## Limitations
+
+- Primarily optimized for Russian language feedback
+- May require fine-tuning for specific regional contexts
+- Best suited for food delivery domain specifically