Refresh split-safe VeriSci dataset
Browse files- LICENSE +157 -0
- README.md +29 -10
- stats.json +24 -20
- test.jsonl +0 -0
- train.jsonl +0 -0
- validation.jsonl +0 -0
LICENSE
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END OF TERMS AND CONDITIONS
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Copyright 2026 VeriSci Team
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README.md
CHANGED
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@@ -48,6 +48,7 @@ Every row is generated by a deterministic Python program and checked by a verifi
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- `final_answer`: machine-readable target answer.
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- `verifier`: verifier type and parameters.
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- `variables`: source variables used to generate the row.
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- `task_family`: task family.
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- `difficulty`: easy, medium, or hard.
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- `source`: generation provenance.
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## Reproduction
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```bash
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git clone
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cd
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PYTHONPATH=src python3 -m verisci.generate
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-
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```
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## Adaptive Data And AutoScientist
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This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training.
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@@ -96,17 +114,18 @@ Current platform evidence:
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| 12k source preflight dataset ID | `13b1c92a-a96b-4ce7-810b-4368ad6aa234` |
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| 12k source-column audit | Passed: 12,000/12,000 rows preserved `Final: {...}` |
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| AutoScientist recommendation | `google/gemma-3-4b-it`, 1-epoch LoRA |
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Fill after final AutoScientist run:
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| Metric | Value |
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|---|---:|
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| Adaptive Data grade before |
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| Adaptive Data grade after |
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| Adaptive Data quality improvement |
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| AutoScientist best win rate |
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| Domain augmentation rows |
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| General augmentation rows |
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## Limitations
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- `final_answer`: machine-readable target answer.
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- `verifier`: verifier type and parameters.
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- `variables`: source variables used to generate the row.
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+
- `dedupe_signature`: canonical hash used for de-duplication and split assignment.
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- `task_family`: task family.
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- `difficulty`: easy, medium, or hard.
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- `source`: generation provenance.
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## Reproduction
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```bash
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git clone <repository-url>
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cd verisci-autoscientist
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PYTHONPATH=src python3 -m verisci.generate \
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--rows 8000 \
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--out data/generated/verisci_8k.jsonl \
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--csv data/generated/verisci_8k.csv \
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--summary data/generated/verisci_8k_summary.json
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PYTHONPATH=src python3 -m verisci.evaluate --data data/generated/verisci_8k.jsonl
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```
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## Release Integrity
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The current 8k public release is split-safe:
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| Metric | Value |
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|---|---:|
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| Rows | 8,000 |
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| Unique prompts | 8,000 |
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| Duplicate prompts | 0 |
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| Train/validation/test prompt leakage rows | 0 |
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| Dedupe signatures | 8,000 |
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| Gold verifier accuracy | 100% |
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## Adaptive Data And AutoScientist
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This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training.
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| 114 |
| 12k source preflight dataset ID | `13b1c92a-a96b-4ce7-810b-4368ad6aa234` |
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| 12k source-column audit | Passed: 12,000/12,000 rows preserved `Final: {...}` |
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| AutoScientist recommendation | `google/gemma-3-4b-it`, 1-epoch LoRA |
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| Active fallback AutoScientist run | `43b5486d-0bfc-4e9f-869e-a9892a679386` on `meta-llama/Llama-3.2-3B-Instruct` |
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Fill after final AutoScientist run:
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| Metric | Value |
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|---|---:|
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| 123 |
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| Adaptive Data grade before | Pending |
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+
| Adaptive Data grade after | Pending |
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| Adaptive Data quality improvement | Pending |
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| AutoScientist best win rate | Pending checkpoint |
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| Domain augmentation rows | 8,000 source rows in active fallback |
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| General augmentation rows | 0 in active fallback |
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## Limitations
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|
stats.json
CHANGED
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{
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"gold_verifier_accuracy": 1.0,
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"rows": 8000,
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"source_file": "data/generated/verisci_8k.jsonl",
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"splits": {
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-
"test":
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"train":
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"validation":
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},
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"task_families": {
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"chemistry_solutions":
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-
"chemistry_stoichiometry":
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-
"code_abstention":
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-
"dimensional_analysis":
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-
"electric_circuits":
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-
"exponential_decay":
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-
"finite_difference_pde":
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-
"linear_modeling":
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-
"numerical_integration":
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"numerical_ode":
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-
"python_code_generation":
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"scientific_abstention":
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-
"thermodynamics":
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"unit_checked_mechanics":
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-
"unit_conversion":
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-
"vector_reasoning":
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-
}
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}
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{
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"dedupe_signatures": 8000,
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"duplicate_prompts": 0,
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| 4 |
"gold_verifier_accuracy": 1.0,
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"rows": 8000,
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| 6 |
"source_file": "data/generated/verisci_8k.jsonl",
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"split_prompt_leakage_rows": 0,
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"splits": {
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"test": 823,
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| 10 |
+
"train": 6409,
|
| 11 |
+
"validation": 768
|
| 12 |
},
|
| 13 |
"task_families": {
|
| 14 |
+
"chemistry_solutions": 447,
|
| 15 |
+
"chemistry_stoichiometry": 421,
|
| 16 |
+
"code_abstention": 190,
|
| 17 |
+
"dimensional_analysis": 447,
|
| 18 |
+
"electric_circuits": 447,
|
| 19 |
+
"exponential_decay": 447,
|
| 20 |
+
"finite_difference_pde": 447,
|
| 21 |
+
"linear_modeling": 447,
|
| 22 |
+
"numerical_integration": 447,
|
| 23 |
+
"numerical_ode": 447,
|
| 24 |
+
"python_code_generation": 447,
|
| 25 |
+
"scientific_abstention": 253,
|
| 26 |
+
"thermodynamics": 894,
|
| 27 |
+
"unit_checked_mechanics": 1341,
|
| 28 |
+
"unit_conversion": 431,
|
| 29 |
+
"vector_reasoning": 447
|
| 30 |
+
},
|
| 31 |
+
"unique_prompts": 8000
|
| 32 |
}
|
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