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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
756 x 322 | 243432 | longmult_3dig | longmult | [
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... | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146116 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
726 x 135 | 98010 | longmult_3dig | longmult | [
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... | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146126 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
520 x 133 | 69160 | longmult_3dig | longmult | [
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1... | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146128 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
420 x 680 | 285600 | longmult_3dig | longmult | [
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... | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146130 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
835 x 429 | 358215 | longmult_3dig | longmult | [
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... | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146131 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
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] | [["540101","540101"],["25137","25137","470611"],["420437","468470","468470"],["(473621)","473621","4(...TRUNCATED) | "[[{\"pattern_used\": \"answer tag\", \"confidence\": 1.0, \"position\": 3347, \"span_text\": \"5401(...TRUNCATED) | "[[{\"evaluation_method\": \"legacy\", \"legacy_result\": false}, {\"evaluation_method\": \"legacy\"(...TRUNCATED) | {"flips_by":[0,0,0,0],"flips_total":0,"num_correct":1,"pass_at_n":1,"percent_correct":0.25,"skill_co(...TRUNCATED) | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146133 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
451 x 158 | 71258 | longmult_3dig | longmult | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"arg1\\\": 451, \\\"arg2\\\": 158, \\\"solution\\\": 71258}\", \"task_source\"(...TRUNCATED) | test | {"arg1": 451, "arg2": 158, "solution": 71258} | ["<think><sample>\nFirst, I need to multiply these two numbers: 451 and 158.\nI can do this either t(...TRUNCATED) | [
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308 x 984 | 303072 | longmult_3dig | longmult | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"arg1\\\": 308, \\\"arg2\\\": 984, \\\"solution\\\": 303072}\", \"task_source\(...TRUNCATED) | test | {"arg1": 308, "arg2": 984, "solution": 303072} | ["<think><sample>\nFirst, I need to multiply 308 by 984. Let me calculate that directly.\nMultiply 3(...TRUNCATED) | [
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600 x 871 | 522600 | longmult_3dig | longmult | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"arg1\\\": 600, \\\"arg2\\\": 871, \\\"solution\\\": 522600}\", \"task_source\(...TRUNCATED) | test | {"arg1": 600, "arg2": 871, "solution": 522600} | ["<think><sample>\nFirst, I need to multiply 600 and 871. That should be straightforward.\n<action>\(...TRUNCATED) | [
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] | [["522600"],["(your answer)","53000","53000"],["...","600 × 871","600 × 871","600 × 871 = 4603400(...TRUNCATED) | "[[{\"pattern_used\": \"answer tag\", \"confidence\": 1.0, \"position\": 2231, \"span_text\": \"5226(...TRUNCATED) | "[[{\"evaluation_method\": \"legacy\", \"legacy_result\": true}], [{\"evaluation_method\": \"legacy\(...TRUNCATED) | {"flips_by":[0,0,0,0],"flips_total":0,"num_correct":1,"pass_at_n":1,"percent_correct":0.25,"skill_co(...TRUNCATED) | 2025-11-02 | test | main | TAUR-dev/M-r1_translated_BASELINE-sft | 0 | eval_sft | 1 | 2025-11-02T01:16:42.146138 | TAUR-dev/D-EVAL__standard_eval_v3__Fin16kEval_SFT_r1_translated-longmult_3dig-eval_sft |
414 x 772 | 319608 | longmult_3dig | longmult | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"arg1\\\": 414, \\\"arg2\\\": 772, \\\"solution\\\": 319608}\", \"task_source\(...TRUNCATED) | test | {"arg1": 414, "arg2": 772, "solution": 319608} | ["<think><sample>\nFirst, I need to multiply these two numbers: 414 and 772. That seems like a large(...TRUNCATED) | [
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End of preview. Expand in Data Studio
Experiment Tracker: Fin16kEval_SFT_r1_translated-longmult_3dig
Experiment Description: Evaluation experiment for task longmult_3dig from Fin16kEval_SFT_r1_translated
Start Time: 2025-11-02T01:32:27.275522
Tracker Dataset: TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__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__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__Fin16kEval_SFT_r1_translated-longmult_3dig__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 - Fin16kEval_SFT_r1_translated-longmult_3dig - {stage_name} - {SFT/RL}
Generated by SkillFactory Experiment Management System All artifacts uploaded immediately as stages complete with perfect data provenance
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