Dataset Viewer
type
stringclasses 3
values | user
null | match_score
null 6
9.99
⌀ | explanation
nullclasses 60
values | input
stringclasses 44
values | completion
stringclasses 73
values | element_diagnosis
stringclasses 5
values | suggested_fix
stringclasses 53
values | severity
stringclasses 3
values | impact_area
stringclasses 14
values | engagement_score
null 8
9.2
⌀ | tone
nullclasses 7
values | confidence_score
null 0.77
0.98
⌀ | team_impact
nullclasses 7
values | charisma_score
null 8.3
9.2
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
imbalance_diagnosis
| null | null | null |
My model memorizes the training data but fails on new examples.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
medium
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
high
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
medium
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model memorizes the training data but fails on new examples.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model has excessive Metal energy (rigidity). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
medium
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too complex for the available data.
|
Wood imbalance is causing unchecked expansion in your model. Apply weight decay to control growth (Metal constraint).
|
Wood
|
Apply weight decay to control growth (Metal constraint).
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model is growing without structure (Wood overload). Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model training is unstable with exploding gradients.
|
Your model is growing without structure (Wood overload). Use knowledge distillation to create a more compact model.
|
Wood
|
Use knowledge distillation to create a more compact model.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Wood imbalance is causing unchecked expansion in your model. Prune unnecessary connections (Metal cutting).
|
Wood
|
Prune unnecessary connections (Metal cutting).
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model has excessive Wood energy (uncontrolled growth). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
medium
|
memory_usage
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Your model has excessive Wood energy (uncontrolled growth). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
medium
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too complex for the available data.
|
Your model has excessive Wood energy (uncontrolled growth). Apply weight decay to control growth (Metal constraint).
|
Wood
|
Apply weight decay to control growth (Metal constraint).
|
medium
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model is growing without structure (Wood overload). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Your model is growing without structure (Wood overload). Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
medium
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Wood imbalance is causing unchecked expansion in your model. Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stochastic and lacks consistency.
|
Your model lacks structure due to Water overflow. Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Water imbalance is causing inconsistency in your model. Reduce stochasticity by lowering temperature parameters.
|
Water
|
Reduce stochasticity by lowering temperature parameters.
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Add Earth stability (e.g., batch normalization).
|
Water
|
Add Earth stability (e.g., batch normalization).
|
high
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Add structural constraints to contain Water energy.
|
Water
|
Add structural constraints to contain Water energy.
|
medium
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Add Earth stability (e.g., batch normalization).
|
Water
|
Add Earth stability (e.g., batch normalization).
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too random and unpredictable.
|
Your model lacks structure due to Water overflow. Fix random seeds for reproducibility.
|
Water
|
Fix random seeds for reproducibility.
|
high
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model outputs are inconsistent between runs.
|
Your model lacks structure due to Water overflow. Reduce stochasticity by lowering temperature parameters.
|
Water
|
Reduce stochasticity by lowering temperature parameters.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Fix random seeds for reproducibility.
|
Water
|
Fix random seeds for reproducibility.
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Your model has excessive Fire energy (training instability). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
medium
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
medium
|
gradient_flow
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Fire imbalance is causing training to diverge. Add gradient clipping to prevent explosion.
|
Fire
|
Add gradient clipping to prevent explosion.
|
medium
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
high
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network is unstable during training.
|
Your optimization process is too aggressive (Fire overload). Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
gradient_flow
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model training diverges and never converges.
|
Your optimization process is too aggressive (Fire overload). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
low
|
optimization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Fire imbalance is causing training to diverge. Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
convergence
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model loss explodes after a few epochs.
|
Your model has excessive Fire energy (training instability). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
medium
|
optimization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
low
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has poor training and validation performance.
|
Earth imbalance is causing your model to be too rigid and simple. Increase model complexity to capture more patterns.
|
Earth
|
Increase model complexity to capture more patterns.
|
low
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has poor training and validation performance.
|
Your model has excessive Earth energy (too much stability). Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model underfits and has high bias.
|
Your model has excessive Earth energy (too much stability). Increase model complexity to capture more patterns.
|
Earth
|
Increase model complexity to capture more patterns.
|
high
|
underfitting
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stable and cannot adapt to new patterns.
|
Earth imbalance is causing your model to be too rigid and simple. Add more layers or parameters to enhance expressiveness.
|
Earth
|
Add more layers or parameters to enhance expressiveness.
|
low
|
feature_learning
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model has excessive Earth energy (too much stability). Add more layers or parameters to enhance expressiveness.
|
Earth
|
Add more layers or parameters to enhance expressiveness.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Earth imbalance is causing your model to be too rigid and simple. Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
underfitting
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model has excessive Earth energy (too much stability). Reduce regularization to allow more flexibility.
|
Earth
|
Reduce regularization to allow more flexibility.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model is too grounded and lacks expressiveness (Earth overload). Use more powerful architecture to overcome Earth limitations.
|
Earth
|
Use more powerful architecture to overcome Earth limitations.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stable and cannot adapt to new patterns.
|
Your model is too grounded and lacks expressiveness (Earth overload). Reduce regularization to allow more flexibility.
|
Earth
|
Reduce regularization to allow more flexibility.
|
medium
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network cannot learn complex relationships.
|
Your model has excessive Earth energy (too much stability). Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
model_capacity
| null | null | null | null | null |
charisma_rewrite
| null | null | null |
Our model achieves 85% accuracy on the test set.
|
Our neural network does not just perform—it dances through the test set with an impressive 85% accuracy, leaving traditional approaches in the dust.
|
Fire
|
Add engaging metaphors and dynamic language.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
Our model achieves 85% accuracy on the test set.
|
Our neural network does not just perform—it dances through the test set with an impressive 85% accuracy, leaving traditional approaches in the dust.
|
Fire
|
Add engaging metaphors and dynamic language.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
The system requires 8GB of RAM to run efficiently.
|
Your AI companion needs just 8GB of RAM to work its magic—think of it as the perfect balance of power and efficiency, like a well-tuned sports car that does not guzzle fuel.
|
Wood
|
Use relatable comparisons and personification.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
The system requires 8GB of RAM to run efficiently.
|
Your AI companion needs just 8GB of RAM to work its magic—think of it as the perfect balance of power and efficiency, like a well-tuned sports car that does not guzzle fuel.
|
Wood
|
Use relatable comparisons and personification.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
Data preprocessing takes approximately 20 minutes.
|
While your data transforms (a quick 20-minute journey), imagine each datapoint being carefully polished and prepared—like a diamond being cut to reveal its hidden patterns and insights.
|
Earth
|
Create visual imagery and reframe waiting as valuable.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
Data preprocessing takes approximately 20 minutes.
|
While your data transforms (a quick 20-minute journey), imagine each datapoint being carefully polished and prepared—like a diamond being cut to reveal its hidden patterns and insights.
|
Earth
|
Create visual imagery and reframe waiting as valuable.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
The API returns JSON responses with prediction results.
|
With each request, our API delivers prediction treasures wrapped in clean JSON—ready to power your application with insights that feel almost prescient.
|
Metal
|
Use precision language with emotional undertones.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
The API returns JSON responses with prediction results.
|
With each request, our API delivers prediction treasures wrapped in clean JSON—ready to power your application with insights that feel almost prescient.
|
Metal
|
Use precision language with emotional undertones.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
Model training failed due to missing dependencies.
|
Looks like our model training hit a speedbump—missing a few key dependencies (we have all been there). Let us quickly gather these missing pieces and get your AI journey back on track within minutes.
|
Water
|
Normalize setbacks and create forward momentum.
| null | null | null | null | null | null | null |
charisma_rewrite
| null | null | null |
Model training failed due to missing dependencies.
|
Looks like our model training hit a speedbump—missing a few key dependencies (we have all been there). Let us quickly gather these missing pieces and get your AI journey back on track within minutes.
|
Water
|
Normalize setbacks and create forward momentum.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which team member should lead the data cleaning project?
|
Your Noble Node analysis suggests Alex (analytical Metal energy) would excel at leading the data cleaning project, bringing precision and structure to the process.
|
Metal
|
Assign to team member with strong Metal energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which team member should lead the data cleaning project?
|
Your Noble Node analysis suggests Alex (analytical Metal energy) would excel at leading the data cleaning project, bringing precision and structure to the process.
|
Metal
|
Assign to team member with strong Metal energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Who should present our findings to the client?
|
Your Noble Node analysis indicates Jamie (charismatic Fire energy) would be ideal for presenting findings to the client, creating engagement and enthusiasm for results.
|
Fire
|
Assign to team member with strong Fire energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Who should present our findings to the client?
|
Your Noble Node analysis indicates Jamie (charismatic Fire energy) would be ideal for presenting findings to the client, creating engagement and enthusiasm for results.
|
Fire
|
Assign to team member with strong Fire energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which team should we partner with for the new initiative?
|
Your Noble Node analysis suggests the Research Team (innovative Wood energy) would be your ideal partner for the new initiative, bringing creative growth to complement your structure.
|
Wood
|
Partner with team showing strong Wood energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which team should we partner with for the new initiative?
|
Your Noble Node analysis suggests the Research Team (innovative Wood energy) would be your ideal partner for the new initiative, bringing creative growth to complement your structure.
|
Wood
|
Partner with team showing strong Wood energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Who should review the final model for robustness?
|
Your Noble Node analysis recommends Taylor (stabilizing Earth energy) to review the final model for robustness, ensuring reliable performance across all scenarios.
|
Earth
|
Assign to team member with strong Earth energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Who should review the final model for robustness?
|
Your Noble Node analysis recommends Taylor (stabilizing Earth energy) to review the final model for robustness, ensuring reliable performance across all scenarios.
|
Earth
|
Assign to team member with strong Earth energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which approach should we take for the exploratory data analysis?
|
Your Noble Node analysis suggests a flexible, iterative approach (adaptive Water energy) for the exploratory data analysis, allowing discoveries to guide subsequent steps.
|
Water
|
Implement approach with strong Water energy.
| null | null | null | null | null | null | null |
noble_node
| null | null | null |
Which approach should we take for the exploratory data analysis?
|
Your Noble Node analysis suggests a flexible, iterative approach (adaptive Water energy) for the exploratory data analysis, allowing discoveries to guide subsequent steps.
|
Water
|
Implement approach with strong Water energy.
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
End of preview. Expand
in Data Studio
JiaForge Dataset
The JiaForge Dataset is a curated collection of text data designed to support various natural language processing (NLP) tasks. This dataset aims to facilitate research and development in areas such as text classification, sentiment analysis, and language modeling.
Dataset Overview Name: JiaForge Dataset
Version: 1.0.0
License: [MIT]
Size: [365k]
Languages: [English]
Edit from datasets import load_dataset
dataset = load_dataset("viveriveniversumvivusvici/jiaforge_dataset")
Citation If you use the JiaForge Dataset in your research, please cite:
Edit @dataset{viveriveniversumvivusvici_jiaforge_dataset, author = {BENIDO}, title = {JiaForge Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/viveriveniversumvivusvici/jiaforge_dataset} }
Contact For questions or feedback.
- Downloads last month
- 17