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dataset_info:
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dtype: 'null'
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download_size: 8294
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configs:
- config_name: evals_eval_rl
data_files:
- split: test
path: evals_eval_rl/test-*
- config_name: logs__evaluation_eval_rl
data_files:
- split: train
path: logs__evaluation_eval_rl/train-*
- config_name: metadata
data_files:
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path: metadata/train-*
Experiment Tracker: RC_VarFix_pv_v2
Experiment Description: Simple test experiment for Skill Factory workflows.
Start Time: 2025-09-21T01:37:18.045706
Tracker Dataset: TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1
Stages Completed
Total stages: 1
Models Created
Dataset Configurations
This tracker dataset contains the following configurations with immediate upload as stages complete:
Training Data (Complete Datasets)
Hyperparameters (Complete Configurations)
Logs (Stage-Specific)
Evaluation Results (Complete with Annotations)
Metadata
- experiment_metadata: Timeline and stage information
Usage
Load specific configurations with:
from datasets import load_dataset
# Load experiment metadata
metadata = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__RC_VarFix_pv_v2__v1', 'evals_eval_rl')
Models
Registry
All models from this experiment are automatically registered in the SkillFactory Model Registry with:
- Complete training configuration (hyperparameters, datasets, methods)
- Experiment lineage (links back to this tracker dataset)
- Stage-specific metadata (SFT vs RL training details)
- Structured input data references (training datasets and configurations)
Registry entries follow the naming pattern: Model - RC_VarFix_pv_v2 - {stage_name} - {SFT/RL}
Generated by SkillFactory Experiment Management System All artifacts uploaded immediately as stages complete with perfect data provenance