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import json
import mlcroissant as mlc
from mlcroissant._src.structure_graph.nodes.source import FileProperty
from mlcroissant.scripts import validate
# 1. Base repo
repo = mlc.FileObject(
id="huggingface-repo",
name="huggingface-repo",
description="ProofWalaDataset GitHub repo",
content_url="https://huggingface.co/datasets/amitayusht/ProofWalaDataset",
encoding_formats=["git+https"],
sha256="main",
)
# 2. Single FileSet that includes all .json files recursively
file_set = mlc.FileSet(
id="all-json",
name="all-json",
description="All .json files across datasets and splits.",
contained_in=["huggingface-repo"],
includes="**/*.json", # recursive glob
encoding_formats=["application/json"],
)
# 3. Helper to make fields
def make_field(field_id, name, description, json_path):
return mlc.Field(
id=field_id,
name=name,
description=description,
data_types=[mlc.DataType.TEXT],
source=mlc.Source(
file_set="all-json",
extract=mlc.Extract(json_path=json_path),
),
)
# 4. Standard fields
fields = [
make_field("record/proof_id", "proof_id", "Proof ID", "training_data[*].proof_id"),
make_field("record/goal_description", "goal_description", "Goal description", "training_data[*].goal_description"),
make_field("record/proof_steps", "proof_steps", "Tactic steps", "training_data[*].proof_steps"),
make_field("record/theorem_name", "theorem_name", "Theorem name", "training_data[*].theorem_name"),
make_field("record/project_id", "project_id", "Project ID", "training_data[*].project_id"),
make_field("record/file_path", "file_path", "File path", "training_data[*].file_path"),
]
# fields += [
# make_field(
# "record/start_goal",
# "start_goal",
# "Main goal from start_goals list",
# "training_data[*].start_goals[*].goal",
# ),
# make_field(
# "record/start_hypotheses",
# "start_hypotheses",
# "List of hypotheses from start_goals",
# "training_data[*].start_goals[*].hypotheses",
# ),
# make_field(
# "record/end_goal",
# "end_goal",
# "Main goal from end_goals list",
# "training_data[*].end_goals[*].goal",
# ),
# make_field(
# "record/end_hypotheses",
# "end_hypotheses",
# "List of hypotheses from end_goals",
# "training_data[*].end_goals[*].hypotheses",
# ),
# ]
# 5. Derived fields: dataset family and split from path
fields += [
mlc.Field(
id="record/source_dataset",
name="source_dataset",
description="Dataset family (e.g. lean, coq).",
data_types=[mlc.DataType.TEXT],
source=mlc.Source(
file_set="all-json",
extract=mlc.Extract(file_property=FileProperty.filepath),
transforms=[mlc.Transform(regex="^([^/]+)/.*")],
),
),
mlc.Field(
id="record/split",
name="split",
description="Split name (train/test/eval).",
data_types=[mlc.DataType.TEXT],
source=mlc.Source(
file_set="all-json",
extract=mlc.Extract(file_property=FileProperty.filepath),
transforms=[mlc.Transform(regex="^[^/]+/([^/]+)/.*")],
),
),
]
# 6. RecordSet
record_set = mlc.RecordSet(
id="proof-records",
name="proof-records",
description="All proof records across dataset families and splits.",
fields=fields,
)
metadata = mlc.Metadata(
name="ProofWalaDataset",
description="A dataset of formal theorem-proving steps extracted from Lean, Coq, GeoCoq, MathComp, and more.",
license="https://opensource.org/licenses/MIT",
url="https://huggingface.co/datasets/amitayusht/ProofWalaDataset",
version="1.0.0",
distribution=[repo, file_set],
record_sets=[record_set],
)
json_dict = metadata.to_json()
with open("croissant.json", "w") as f:
json.dump(json_dict, f, indent=2)
# validate.main("croissant.json")
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