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ProofWalaDataset

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train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
training_data_buffer_size: int64
last_training_data: int64
last_proof_id: string
external_theorems_used_cnt: int64
local_theorems_used_cnt: int64
total_proof_step_cnt: int64
data_filename_prefix: string
data_filename_suffix: string
lemma_ref_filename_prefix: string
lemma_ref_filename_suffix: string
num_theorems: int64
vs
training_data: list<item: struct<proof_id: string, all_useful_defns_theorems: list<item: null>, goal_description: null, start_goals: list<item: struct<hypotheses: list<item: string>, goal: string, relevant_defns: list<item: null>, used_theorems_local: list<item: null>, used_theorems_external: list<item: null>, possible_useful_theorems_external: list<item: null>, possible_useful_theorems_local: list<item: null>>>, end_goals: list<item: struct<hypotheses: list<item: string>, goal: string, relevant_defns: list<item: null>, used_theorems_local: list<item: null>, used_theorems_external: list<item: null>, possible_useful_theorems_external: list<item: null>, possible_useful_theorems_local: list<item: null>>>, proof_steps: list<item: string>, simplified_goals: list<item: null>, addition_state_info: struct<>, file_path: string, project_id: string, theorem_name: string>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3339, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2300, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 504, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              training_data_buffer_size: int64
              last_training_data: int64
              last_proof_id: string
              external_theorems_used_cnt: int64
              local_theorems_used_cnt: int64
              total_proof_step_cnt: int64
              data_filename_prefix: string
              data_filename_suffix: string
              lemma_ref_filename_prefix: string
              lemma_ref_filename_suffix: string
              num_theorems: int64
              vs
              training_data: list<item: struct<proof_id: string, all_useful_defns_theorems: list<item: null>, goal_description: null, start_goals: list<item: struct<hypotheses: list<item: string>, goal: string, relevant_defns: list<item: null>, used_theorems_local: list<item: null>, used_theorems_external: list<item: null>, possible_useful_theorems_external: list<item: null>, possible_useful_theorems_local: list<item: null>>>, end_goals: list<item: struct<hypotheses: list<item: string>, goal: string, relevant_defns: list<item: null>, used_theorems_local: list<item: null>, used_theorems_external: list<item: null>, possible_useful_theorems_external: list<item: null>, possible_useful_theorems_local: list<item: null>>>, proof_steps: list<item: string>, simplified_goals: list<item: null>, addition_state_info: struct<>, file_path: string, project_id: string, theorem_name: string>>

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📜 ProofWalaDataset

The ProofWalaDataset is a multilingual dataset of formal theorem proving traces collected from multiple interactive theorem prover (ITP) ecosystems. It provides a structured view of proof steps, goals, hypotheses, and theorem names from diverse mathematical and program verification libraries.

This dataset is intended for researchers and practitioners working on:

Automated theorem proving
Formal code generation
Machine learning for logic
Proof step prediction
Multi-language transfer in formal systems

📂 Dataset Structure

The dataset is organized into the following ITP families:

lean/
coq/
GeoCoq/
math-comp/
multilingual/ (cross-formal-language hybrid)

Each family includes standard splits:
train/, test/, and eval/, each containing multiple JSON files.
Each JSON file contains a top-level key: "training_data" with a list of proof records.

🔍 Each record contains

Field	Description
`proof_id`	Unique identifier for the proof trace
`goal_description`	Optional natural language description of the proof
`start_goals`	List of starting goals (each with `goal` and `hypotheses`)
`end_goals`	Final goals after applying proof steps
`proof_steps`	List of applied proof tactics (`inv`, `rewrite`, etc.)
`simplified_goals`	Simplified representations of goals (if any)
`all_useful_defns_theorems`	Set of useful definitions or theorems (static analysis)
`addition_state_info`	Optional additional metadata about the proof context
`file_path`	Source file where the proof appears
`project_id`	The ITP project or repository path (e.g., CompCert)
`theorem_name`	Name of the theorem being proved

For convenience, structured fields such as start_goals[*].goal, start_goals[*].hypotheses, end_goals[*].goal, and end_goals[*].hypotheses are exposed directly through the Croissant metadata.

🧠 Use Cases

Pretraining and finetuning LLMs for formal verification
Evaluating proof search strategies
Building cross-language proof translators
Fine-grained proof tactic prediction

📊 Format

Data format: JSON
Schema described via Croissant metadata (croissant.json)
Fully validated using mlcroissant

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