Dataset Viewer
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>>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📜 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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