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hugging-face-evaluation-manager Adapted from Hugging Face's evaluation manager skill for FF-Terminal, this skill provides comprehensive tools for managing evaluation results of machine learning models used in agricultural contexts. It supports tracking crop prediction models, yield forecasting systems, disease detection models, and other agricultural ML applications. Includes integration with model cards, benchmark comparisons, and performance tracking over time.
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slug hf_evaluation_manager name Hugging Face Evaluation Manager summary Manage and track model evaluation results for agricultural ML models and farm prediction systems description Adapted from Hugging Face's evaluation manager skill for FF-Terminal, this skill provides comprehensive tools for managing evaluation results of machine learning models used in agricultural contexts. It supports tracking crop prediction models, yield forecasting systems, disease detection models, and other agricultural ML applications. Includes integration with model cards, benchmark comparisons, and performance tracking over time. version 0.1.0 tags ["huggingface","evaluation","metrics","benchmarking","agriculture","machine-learning","model-performance","farming"] triggers ["evaluation","model evaluation","metrics","benchmark","performance","model card","agricultural model","crop model","disease detection","yield prediction"] priority default assets [] recommended_tools ["analyze_data","write_file","run_command","tavily_search","generate_image_gemini"]
Hugging Face Evaluation Manager for FF-Terminal
Overview
This skill enables you to manage, track, and analyze evaluation results for machine learning models used in agricultural applications. It provides tools for adding structured evaluation data to model cards, comparing performance across different models, and maintaining comprehensive evaluation histories.
When to Use This Skill
Use this skill when you need to:
Track evaluation results for crop yield prediction models
Compare performance of different disease detection models
Monitor model accuracy for weather forecasting systems
Add evaluation metrics to agricultural ML model cards
Benchmark models against industry standards
Track model performance over growing seasons
Evaluate precision agriculture algorithms
Key Capabilities
1. Agricultural Model Types Supported
Crop Yield Prediction : Regression models for yield forecastingDisease Detection : Classification models for plant disease identificationWeather Prediction : Time series models for weather and climate forecastingSoil Analysis : Models for soil property prediction and recommendationsPest Detection : Computer vision models for pest identificationIrrigation Optimization : Models for water usage optimizationHarvest Timing : Models for optimal harvest prediction
2. Evaluation Metrics
Regression Metrics : MAE, MSE, RMSE, R² for continuous predictionsClassification Metrics : Accuracy, Precision, Recall, F1-Score, AUC-ROCTime Series Metrics : MAPE, SMAPE, Directional AccuracyCustom Agricultural Metrics : Yield prediction accuracy, disease detection ratesEconomic Metrics : Cost-benefit analysis, ROI calculationsEnvironmental Metrics : Resource efficiency, sustainability scores
3. Data Sources for Evaluation
Historical Farm Data : Past seasons' performance dataCross-Validation : K-fold validation on agricultural datasetsField Trials : Real-world testing on actual farmsSimulation Results : Controlled environment testing
Benchmark Datasets : Standard agricultural ML benchmarks
Prerequisites
Hugging Face account with model repository access
HF_TOKEN environment variable set
Python packages: huggingface_hub, pandas, numpy, scikit-learn
Evaluation data in structured format (CSV, JSON)
Usage Workflow
Step 1: Prepare Evaluation Results Format your evaluation data:
import pandas as pd
eval_results = pd.DataFrame({
"model_name" : ["CropYieldNet_v1" , "CropYieldNet_v2" ],
"mae" : [0.15 , 0.12 ],
"rmse" : [0.22 , 0.18 ],
"r2_score" : [0.87 , 0.91 ],
"dataset" : "US_Corn_Yields_2020-2023" ,
"test_samples" : 1000
})
Step 2: Generate Evaluation Report Create comprehensive evaluation summary:
def generate_eval_report (eval_results, model_name ):
report = f"""
# Model Evaluation Report: {model_name}
## Performance Metrics
- **Mean Absolute Error**: {eval_results['mae' ]:.3 f}
- **Root Mean Square Error**: {eval_results['rmse' ]:.3 f}
- **R² Score**: {eval_results['r2_score' ]:.3 f}
## Dataset Information
- **Test Dataset**: {eval_results['dataset' ]}
- **Sample Size**: {eval_results['test_samples' ]:,}
- **Evaluation Date**: {pd.Timestamp.now().strftime('%Y-%m-%d' )}
## Agricultural Context
This model was evaluated on historical crop yield data spanning multiple
growing seasons and geographic regions. Performance metrics reflect
real-world prediction accuracy for yield forecasting applications.
"""
return report
Step 3: Update Model Card Add evaluation results to model card:
from huggingface_hub import update_repo_metadata
model_card_content = f"""
---
language: en
tags:
- agriculture
- crop-yield
- regression
license: mit
metrics:
- mae
- rmse
- r2
---
# {model_name}
## Model Description
{model_description}
## Evaluation Results
{evaluation_report}
## Usage
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("your-username/{model_name} ")
Update model card update_repo_metadata(
repo_id="your-username/your-model",
token=your_hf_token,
metadata=model_card_content
)
### Step 4: Track Performance Over Time
Maintain evaluation history:
```python
def track_model_performance(model_id, new_results):
# Load existing evaluation history
history_file = f"eval_history_{model_id.replace('/', '_')}.json"
try:
with open(history_file, 'r') as f:
history = json.load(f)
except FileNotFoundError:
history = []
# Add new evaluation results
new_entry = {
"timestamp": datetime.now().isoformat(),
"results": new_results,
"version": get_current_model_version(model_id)
}
history.append(new_entry)
# Save updated history
with open(history_file, 'w') as f:
json.dump(history, f, indent=2)
Agricultural Evaluation Best Practices
Seasonal Validation
Evaluate models across multiple growing seasons
Account for seasonal variations and climate patterns
Test on different geographic regions and soil types
Consider weather variability and extreme events
Real-World Testing
Validate predictions against actual farm outcomes
Include economic impact assessments
Test on diverse farming operations and scales
Consider practical deployment constraints
Benchmark Comparisons
Compare against baseline agricultural models
Include industry-standard benchmarks
Reference academic literature and established methods
Provide context for performance metrics
Integration with FF-Terminal Tools This skill works seamlessly with other FF-Terminal capabilities:
Data Analysis : Use analyze_data tool to explore evaluation resultsVisualization : Create performance charts and comparison plotsModel Training : Integrate with training pipelines for continuous evaluationAutomation : Schedule regular model performance monitoring
Example Use Cases
1. Crop Yield Model Evaluation
yield_model = "CropYieldNet_v2"
test_data = load_yield_test_data("2023_season_data.csv" )
predictions = yield_model.predict(test_data)
metrics = calculate_regression_metrics(test_data["actual_yield" ], predictions)
eval_report = generate_eval_report(metrics, yield_model)
update_model_card("your-org/crop-yield-predictor" , eval_report)
2. Disease Detection Model Comparison
models = ["PlantDiseaseNet_v1" , "PlantDiseaseNet_v2" , "ResNet50-PlantDisease" ]
test_images = load_disease_test_set("plant_disease_test/" )
results = []
for model in models:
predictions = model.predict(test_images)
metrics = calculate_classification_metrics(test_images["labels" ], predictions)
results.append({"model" : model, **metrics})
comparison_report = create_comparison_table(results)
update_model_card("your-org/plant-detection-suite" , comparison_report)
3. Model Performance Tracking
model_id = "your-org/weather-forecaster"
new_eval_results = evaluate_weather_model(model_id, "2024_data" )
track_model_performance(model_id, new_eval_results)
trend_analysis = analyze_performance_trends(model_id)
update_model_card(model_id, trend_analysis)
Advanced Features
Automated Evaluation Pipelines
Set up continuous evaluation on new data
Automated model card updates
Performance alerting and notifications
Integration with CI/CD pipelines
Comparative Analysis
Multi-model comparison reports
Statistical significance testing
Performance ranking and leaderboards
Cross-dataset generalization analysis
Economic Impact Assessment
Cost-benefit analysis for model deployment
ROI calculations for agricultural applications
Resource optimization recommendations
Risk assessment and mitigation strategies
Troubleshooting
Common Issues
Metric calculation errors : Verify data formats and column namesModel card update failures : Check repository permissions and token validityPerformance inconsistencies : Ensure consistent evaluation protocolsData quality issues : Validate test data quality and representativeness
Error Recovery
Implement validation checks for evaluation data
Use backup evaluation methods for edge cases
Maintain audit trails of evaluation processes
Provide clear error messages and recovery guidance
Resources 