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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 | |
| } | |
| } |