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{
"name": "prevent_model_collapse",
"description": "Preemptive safeguard against model collapse, ensuring consistent learning and retention of data integrity across model generations.",
"strict": true,
"parameters": {
"type": "object",
"required": [
"initial_data",
"training_steps",
"model_capacities",
"sampling_method"
],
"properties": {
"initial_data": {
"type": "array",
"description": "The initial clean data used for training the first model (model 0).",
"items": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Text data sample that will be used for training."
},
"label": {
"type": "string",
"description": "Label associated with the data sample."
}
},
"additionalProperties": false,
"required": [
"text",
"label"
]
}
},
"training_steps": {
"type": "number",
"description": "Number of iterations for training the models to evaluate convergence and performance."
},
"model_capacities": {
"type": "object",
"required": [
"max_samples",
"memory_limit"
],
"properties": {
"max_samples": {
"type": "number",
"description": "Maximum number of samples to retain for each model's training dataset."
},
"memory_limit": {
"type": "number",
"description": "Memory limit for training each individual model."
}
},
"additionalProperties": false
},
"sampling_method": {
"type": "string",
"description": "Method used for data sampling during each training phase.",
"enum": [
"Monte_Carlo",
"stratified",
"random"
]
}
},
"additionalProperties": false
}
} |