# Model Name
## Function Description
- Main Purpose: Text Sentiment Analysis
- Supported Languages: Chinese
- Input Format: Text String
- Output Format: Sentiment Labels (Positive/Negative/Neutral)

## Technical Details
- Framework: PyTorch
- Pre-trained Model: BERT-base-chinese
- Model Size: 110M
- Inference Speed: 100ms/sentence

## Usage Examples
```python
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="username/bert-chinese-sentiment")
result = classifier("This product is very good!")

**3. Model Configuration File Optimization**
```yaml
# model-config.yaml
model-name: bert-chinese-sentiment
version: 1.0.0
framework: pytorch
task: text-classification
tags:
  - chinese
  - sentiment-analysis
  - bert
  - nlp

dependencies:
  - torch>=1.9.0
  - transformers>=4.20.0
  - numpy>=1.21.0

model-info:
  architecture: bert-base-chinese
  parameters: 110M
  max-input-length: 512
  
performance:
  inference-time: 100ms
  accuracy: 0.92
  f1-score: 0.91

usage-examples:
  - input: "This product is very good!"
    output: "Positive"
  - input: "Quality is too poor"
    output: "Negative"

# Minor version update
git tag v1.0.1
git push origin v1.0.1

# Feature update
git tag v1.1.0
git push origin v1.1.0

# Major update
git tag v2.0.0
git push origin v2.0.0

# Changelog

## [1.1.0] - 2024-01-15
### Added
- Support for batch inference
- Added quantized model version
- Optimized inference speed

### Fixed
- Fixed long text processing issues
- Resolved memory leak problems

### Changed
- Updated dependency library versions
- Optimized model structure

## [1.0.1] - 2024-01-01
### Fixed
- Fixed input validation issues
- Resolved encoding problems

dataset-name/
├── README.md
├── data/
│   ├── train/
│   ├── validation/
│   └── test/
├── metadata.json
├── schema.json
└── examples/
    ├── sample1.jpg
    ├── sample2.jpg
    └── sample3.jpg

{
  "name": "chinese-text-classification",
  "version": "1.0.0",
  "description": "Chinese Text Classification Dataset",
  "license": "MIT",
  "creator": "username",
  "created_date": "2024-01-01",
  "last_updated": "2024-01-15",
  
  "statistics": {
    "total_samples": 10000,
    "train_samples": 8000,
    "validation_samples": 1000,
    "test_samples": 1000,
    "classes": 5
  },
  
  "format": {
    "input_type": "text",
    "output_type": "label",
    "encoding": "utf-8"
  },
  
  "quality_metrics": {
    "completeness": 0.98,
    "consistency": 0.95,
    "accuracy": 0.92
  }
}

# Data quality check script
import pandas as pd
import numpy as np
from typing import Dict, List

def check_data_quality(data: pd.DataFrame) -> Dict[str, float]:
    """Check data quality"""
    quality_metrics = {}
    
    # Completeness check
    quality_metrics['completeness'] = 1 - data.isnull().sum().sum() / (data.shape[0] * data.shape[1])
    
    # Consistency check
    quality_metrics['consistency'] = check_data_consistency(data)
    
    # Accuracy check
    quality_metrics['accuracy'] = check_data_accuracy(data)
    
    return quality_metrics

def check_data_consistency(data: pd.DataFrame) -> float:
    """Check data consistency"""
    # Implement consistency check logic
    pass

def check_data_accuracy(data: pd.DataFrame) -> float:
    """Check data accuracy"""
    # Implement accuracy check logic
    pass

# Dataset Name

## Overview
Briefly describe the dataset content, purpose, and characteristics.

## Data Source
Explain data source, collection methods, and time.

## Data Format
Describe data format, structure, and field descriptions in detail.

## Usage Instructions
Provide examples of data loading, preprocessing, and usage.

## Quality Assessment
Explain data quality assessment results and considerations.

## License
Explain data usage license and restrictions.

## Citation
If dataset comes from research papers, provide citation information.

## Contact
Provide contact information for dataset maintainers.

# app.py
from flask import Flask, request, jsonify
from modules.preprocessor import DataPreprocessor
from modules.model import ModelWrapper
from modules.postprocessor import ResultPostprocessor

app = Flask(__name__)

# Initialize components
preprocessor = DataPreprocessor()
model = ModelWrapper()
postprocessor = ResultPostprocessor()

@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Data preprocessing
        input_data = request.json
        processed_data = preprocessor.process(input_data)
        
        # Model inference
        raw_result = model.predict(processed_data)
        
        # Result post-processing
        final_result = postprocessor.process(raw_result)
        
        return jsonify({
            'success': True,
            'result': final_result
        })
    except Exception as e:
        return jsonify({
            'success': False,
            'error': str(e)
        }), 400

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

# app.yaml
name: text-classification-app
version: 1.0.0
description: Text Classification Application

runtime: python3.9
entrypoint: app:app

env_variables:
  MODEL_PATH: /app/models/model.pkl
  MAX_INPUT_LENGTH: 512
  BATCH_SIZE: 32

resources:
  cpu: 2
  memory: 4Gi
  gpu: 1

dependencies:
  - flask==2.3.0
  - torch==2.0.0
  - transformers==4.30.0

health_check:
  path: /health
  interval: 30s
  timeout: 10s
  retries: 3

import functools
import redis
import pickle

# Redis cache decorator
def cache_result(expire_time=3600):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            # Generate cache key
            cache_key = f"{func.__name__}:{hash(str(args) + str(kwargs))}"
            
            # Try to get from cache
            cached_result = redis_client.get(cache_key)
            if cached_result:
                return pickle.loads(cached_result)
            
            # Execute function and cache result
            result = func(*args, **kwargs)
            redis_client.setex(cache_key, expire_time, pickle.dumps(result))
            
            return result
        return wrapper
    return decorator

@cache_result(expire_time=1800)
def expensive_computation(input_data):
    # Expensive computation logic
    pass

import asyncio
from concurrent.futures import ThreadPoolExecutor
import aiohttp

class AsyncModelService:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=4)
    
    async def batch_predict(self, input_list):
        """Asynchronous batch prediction"""
        tasks = []
        for input_data in input_list:
            task = asyncio.create_task(self.predict_single(input_data))
            tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return results
    
    async def predict_single(self, input_data):
        """Single prediction task"""
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            self.executor, 
            self._predict, 
            input_data
        )
        return result
    
    def _predict(self, input_data):
        """Actual prediction logic"""
        # Model inference code
        pass

# 1. Imports and Configuration
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Set display options
pd.set_option('display.max_columns', None)
plt.style.use('seaborn-v0_8')

# 2. Data Loading
@load_data
def load_dataset():
    """Load dataset"""
    data = pd.read_csv('dataset.csv')
    print(f"Dataset shape: {data.shape}")
    return data

# 3. Data Exploration
def explore_data(data):
    """Data exploration analysis"""
    print("Data basic information:")
    print(data.info())
    
    print("\nData statistical summary:")
    print(data.describe())
    
    print("\nMissing value statistics:")
    print(data.isnull().sum())

# 4. Data Preprocessing
def preprocess_data(data):
    """Data preprocessing"""
    # Handle missing values
    data = data.fillna(data.median())
    
    # Feature engineering
    data['feature_new'] = data['feature1'] * data['feature2']
    
    return data

# 5. Model Training
def train_model(X, y):
    """Model training"""
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )
    
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    
    return model, X_test, y_test

# 6. Result Evaluation
def evaluate_model(model, X_test, y_test):
    """Model evaluation"""
    from sklearn.metrics import classification_report, confusion_matrix
    
    y_pred = model.predict(X_test)
    
    print("Classification Report:")
    print(classification_report(y_test, y_pred))
    
    # Plot confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d')
    plt.title('Confusion Matrix')
    plt.show()

class DataProcessor:
    """Data processing class"""
    
    def __init__(self, config):
        self.config = config
        self.data = None
    
    def load(self, file_path):
        """Load data"""
        if file_path.endswith('.csv'):
            self.data = pd.read_csv(file_path)
        elif file_path.endswith('.json'):
            self.data = pd.read_json(file_path)
        else:
            raise ValueError("Unsupported file format")
        
        return self.data
    
    def clean(self):
        """Data cleaning"""
        if self.data is None:
            raise ValueError("Please load data first")
        
        # Remove duplicate rows
        self.data = self.data.drop_duplicates()
        
        # Handle missing values
        self.data = self.data.fillna(self.config.get('fill_method', 'median'))
        
        return self.data
    
    def transform(self):
        """Data transformation"""
        if self.data is None:
            raise ValueError("Please load data first")
        
        # Feature engineering
        for feature in self.config.get('features', []):
            if feature['type'] == 'categorical':
                self.data = pd.get_dummies(self.data, columns=[feature['name']])
            elif feature['type'] == 'numerical':
                self.data[feature['name']] = pd.to_numeric(self.data[feature['name']])
        
        return self.data

# Usage example
config = {
    'fill_method': 'mean',
    'features': [
        {'name': 'category', 'type': 'categorical'},
        {'name': 'price', 'type': 'numerical'}
    ]
}

processor = DataProcessor(config)
data = processor.load('data.csv').clean().transform()

import mlflow
import os

# Set up MLflow
mlflow.set_tracking_uri("sqlite:///mlflow.db")
mlflow.set_experiment("Text Classification Experiment")

def run_experiment(params):
    """Run experiment"""
    with mlflow.start_run():
        # Log parameters
        mlflow.log_params(params)
        
        # Train model
        model = train_model_with_params(params)
        
        # Evaluate model
        metrics = evaluate_model(model)
        
        # Log metrics
        mlflow.log_metrics(metrics)
        
        # Save model
        mlflow.sklearn.log_model(model, "model")
        
        # Log data version
        mlflow.log_artifact("data.csv")
        
        return model, metrics

# Experiment parameters
experiment_params = [
    {'model': 'random_forest', 'n_estimators': 100},
    {'model': 'random_forest', 'n_estimators': 200},
    {'model': 'xgboost', 'n_estimators': 100}
]

# Run experiments
results = []
for params in experiment_params:
    model, metrics = run_experiment(params)
    results.append({'params': params, 'metrics': metrics})

# Compare results
results_df = pd.DataFrame(results)
print(results_df)

# Save checkpoint
def save_checkpoint(notebook, name, description=""):
    """Save checkpoint"""
    checkpoint = {
        'name': name,
        'description': description,
        'timestamp': pd.Timestamp.now(),
        'variables': notebook.user_ns.copy(),
        'outputs': notebook.cell_outputs.copy()
    }
    
    # Save to file
    checkpoint_file = f"checkpoints/{name}.pkl"
    os.makedirs("checkpoints", exist_ok=True)
    
    with open(checkpoint_file, 'wb') as f:
        pickle.dump(checkpoint, f)
    
    print(f"Checkpoint saved: {checkpoint_file}")

# Restore checkpoint
def restore_checkpoint(notebook, name):
    """Restore checkpoint"""
    checkpoint_file = f"checkpoints/{name}.pkl"
    
    if not os.path.exists(checkpoint_file):
        print(f"Checkpoint doesn't exist: {checkpoint_file}")
        return False
    
    with open(checkpoint_file, 'rb') as f:
        checkpoint = pickle.load(f)
    
    # Restore variables
    notebook.user_ns.update(checkpoint['variables'])
    
    print(f"Checkpoint restored: {name}")
    print(f"Description: {checkpoint['description']}")
    print(f"Time: {checkpoint['timestamp']}")
    
    return True

# Code style guide
"""
Code Style Standards:
1. Use meaningful variable names and function names
2. Add appropriate comments and docstrings
3. Follow PEP 8 code style
4. Use type hints
5. Write unit tests
"""

from typing import List, Dict, Optional, Union
import numpy as np
import pandas as pd

def process_text_data(
    text_list: List[str],
    max_length: int = 512,
    tokenizer: Optional[object] = None
) -> Dict[str, np.ndarray]:
    """
    Process text data
    
    Args:
        text_list: List of texts
        max_length: Maximum length
        tokenizer: Tokenizer object
    
    Returns:
        Dictionary containing processed data
    
    Raises:
        ValueError: When input parameters are invalid
    """
    if not text_list:
        raise ValueError("Text list cannot be empty")
    
    # Processing logic
    processed_data = {}
    
    return processed_data

# Branch management strategy
# main: Main branch, keep stable
# develop: Development branch, integrate features
# feature/*: Feature branches, develop new features
# hotfix/*: Hotfix branches, fix urgent issues

# Create feature branch
git checkout -b feature/text-classification

# Commit after development completion
git add .
git commit -m "feat: Add text classification feature

- Implement basic text classification model
- Add data preprocessing functionality
- Integrate evaluation metrics"

# Push to remote
git push origin feature/text-classification

# Create merge request
# Create MR/PR on GitLab/GitHub

project/
├── README.md
├── docs/
│   ├── api/
│   ├── user-guide/
│   └── development/
├── examples/
├── tests/
└── requirements.txt

# Documentation Title

## Overview
Briefly describe the function or module purpose.

## Features
- Feature 1: Description
- Feature 2: Description

## Usage
Provide detailed usage instructions and example code.

## API Reference
Detailed API interface and parameter descriptions.

## Notes
Explain usage considerations and limitations.

## FAQ
List common questions and solutions.

## Changelog
Record version update information.

# Environment variable management
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Get sensitive configuration
API_KEY = os.getenv('API_KEY')
DATABASE_URL = os.getenv('DATABASE_URL')

# Validate configuration
if not API_KEY:
    raise ValueError("API_KEY environment variable not set")

# .env file (don't commit to version control)
API_KEY=your_api_key_here
DATABASE_URL=your_database_url_here

from pydantic import BaseModel, validator
from typing import List

class InputData(BaseModel):
    """Input data model"""
    text: str
    max_length: int = 512
    
    @validator('text')
    def validate_text(cls, v):
        if not v or len(v.strip()) == 0:
            raise ValueError('Text cannot be empty')
        if len(v) > 10000:
            raise ValueError('Text length cannot exceed 10000 characters')
        return v.strip()
    
    @validator('max_length')
    def validate_max_length(cls, v):
        if v <= 0 or v > 10000:
            raise ValueError('Maximum length must be between 1-10000')
        return v

# Usage example
try:
    data = InputData(text="Test text", max_length=100)
    print("Data validation passed")
except ValueError as e:
    print(f"Data validation failed: {e}")

from functools import wraps
from flask import request, jsonify

def require_auth(f):
    """Authentication decorator"""
    @wraps(f)
    def decorated_function(*args, **kwargs):
        token = request.headers.get('Authorization')
        
        if not token:
            return jsonify({'error': 'Missing authentication token'}), 401
        
        if not validate_token(token):
            return jsonify({'error': 'Invalid authentication token'}), 401
        
        return f(*args, **kwargs)
    return decorated_function

def require_role(role):
    """Role validation decorator"""
    def decorator(f):
        @wraps(f)
        def decorated_function(*args, **kwargs):
            user_role = get_user_role(request.headers.get('Authorization'))
            
            if user_role != role:
                return jsonify({'error': 'Insufficient permissions'}), 403
            
            return f(*args, **kwargs)
        return decorated_function
    return decorator

# Usage example
@app.route('/admin', methods=['GET'])
@require_auth
@require_role('admin')
def admin_panel():
    return jsonify({'message': 'Welcome to admin panel'})

import psutil
import time
from datetime import datetime

class PerformanceMonitor:
    """Performance monitor"""
    
    def __init__(self):
        self.metrics = []
    
    def collect_metrics(self):
        """Collect performance metrics"""
        metrics = {
            'timestamp': datetime.now(),
            'cpu_percent': psutil.cpu_percent(interval=1),
            'memory_percent': psutil.virtual_memory().percent,
            'disk_usage': psutil.disk_usage('/').percent
        }
        
        self.metrics.append(metrics)
        return metrics
    
    def get_summary(self):
        """Get performance summary"""
        if not self.metrics:
            return {}
        
        cpu_values = [m['cpu_percent'] for m in self.metrics]
        memory_values = [m['memory_percent'] for m in self.metrics]
        
        return {
            'cpu_avg': sum(cpu_values) / len(cpu_values),
            'cpu_max': max(cpu_values),
            'memory_avg': sum(memory_values) / len(memory_values),
            'memory_max': max(memory_values)
        }

# Use monitor
monitor = PerformanceMonitor()

# Collect metrics periodically
while True:
    metrics = monitor.collect_metrics()
    print(f"CPU: {metrics['cpu_percent']}%, Memory: {metrics['memory_percent']}%")
    time.sleep(60)  # Collect once per minute

import time
from functools import wraps

def performance_monitor(func):
    """Performance monitoring decorator"""
    @wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        
        try:
            result = func(*args, **kwargs)
            execution_time = time.time() - start_time
            
            # Log performance metrics
            log_performance(func.__name__, execution_time, 'success')
            
            return result
        except Exception as e:
            execution_time = time.time() - start_time
            
            # Log error and performance metrics
            log_performance(func.__name__, execution_time, 'error', str(e))
            raise
    
    return wrapper

def log_performance(func_name, execution_time, status, error=None):
    """Log performance logs"""
    log_entry = {
        'function': func_name,
        'execution_time': execution_time,
        'status': status,
        'timestamp': datetime.now(),
        'error': error
    }
    
    # Write to log file or database
    print(f"Performance log: {log_entry}")

# Usage example
@performance_monitor
def process_large_dataset(data):
    """Process large dataset"""
    time.sleep(2)  # Simulate processing time
    return len(data)

# Test performance monitoring
result = process_large_dataset(range(1000))