docs: make dataset card general
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README.md
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# LeanTransitionCorpus
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> **Status: incremental extraction is substantially complete.** The four pinned
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> source streams have reached their declared ends and verified shards are
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> published. One Mathlib batch (`mathlib/001787`) is quarantined for a repair
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> and is not included in the clean splits; the corpus should be treated as
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> incomplete until that batch is resolved.
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LeanTransitionCorpus is a dataset for training and studying automated theorem
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proving systems in Lean. Its unit of data is one tactic transition: the proof state
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before a tactic, the tactic that was executed, and the resulting state. This makes
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The extraction pipeline draws from pinned snapshots of the following Lean sources.
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Rows identify their origin in `source_dataset`, `source_url`, `repo_commit`, and
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toolchain columns.
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published coverage than another at any point in time.
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| Source | Role in the corpus |
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|---|---|
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with `json.loads` when you need their tree structure; leave them as strings for
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text-only training baselines.
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Each shard is round-trip
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only after all of its split shards are verified. Failed attempts are described in
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batch evidence and must not be mistaken for successfully extracted proofs.
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Quarantine artifacts, when present under `data/quarantine/`, are excluded from the
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default configuration. They are archival evidence, not clean training examples.
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Splits are inherited from the frozen extractor, not recomputed by the publisher.
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Replay status is recorded per row; publication does not imply replay verification.
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Frozen extraction inputs under `sources/` are also excluded from the default
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configuration. They allow scratch-only workers to range-read deterministic chunks
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when an original upstream revision is no longer available. Their paths, immutable
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revisions, sizes, and SHA-256 digests are recorded by the extraction runner.
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## Read
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Use an immutable revision for experiments. The repository grows over time.
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## Published coverage and local seed snapshot
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The current published repository contains verified Parquet shards from the
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NuminaMath-CoT, DeepSeek-Prover, Goedel Lean Workbook, and pinned Mathlib
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streams. The extraction runner processed approximately 172,000 source proofs
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across its completed production runs. Exact transition-row and token counts are
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not fixed in this incremental repository: they should be read from the Parquet
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files at the immutable revision used by an experiment. The final Mathlib retry
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will add only the repaired batch and will not rewrite existing shards.
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The following is the historical local seed snapshot, retained for provenance;
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it is not a count of the full published repository.
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The imported seed contains 285 completed Numina batches: 1,425 attempted proofs,
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1,378 extracted proofs, and 1,374 source-distinct proofs retained as 13,105
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transition rows. Four extracted proofs failed internal-state validation during
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canonicalization; the batch evidence records these exclusions.
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| Split | Transition rows | Source-distinct proofs |
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| train | 11,182 | 1,159 |
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| dev | 654 | 65 |
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| internal_test | 1,269 | 150 |
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No quarantine transition rows are present in this seed. All transition rows have
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`replay_status=not_run`. The local audit found no duplicate provenance-based
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transition keys or theorem-fingerprint conflicts across splits. These checks do
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not constitute independent Lean replay validation.
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## Identity
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`example_id` is globally unique and deterministic. `proof_id` retains the source
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# LeanTransitionCorpus
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LeanTransitionCorpus is a dataset for training and studying automated theorem
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proving systems in Lean. Its unit of data is one tactic transition: the proof state
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before a tactic, the tactic that was executed, and the resulting state. This makes
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The extraction pipeline draws from pinned snapshots of the following Lean sources.
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Rows identify their origin in `source_dataset`, `source_url`, `repo_commit`, and
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toolchain columns.
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| Source | Role in the corpus |
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|---|---|
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with `json.loads` when you need their tree structure; leave them as strings for
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text-only training baselines.
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Each shard is round-trip checked against its source records before publication,
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and uploaded bytes are verified at an immutable Hugging Face revision. Split
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assignments and provenance are retained so experiments can be reproduced and
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results can be traced back to their source theorem and environment.
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## Read
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Use an immutable revision for experiments. The repository grows over time.
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## Identity
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`example_id` is globally unique and deterministic. `proof_id` retains the source
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