Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
video
multimodal
event-understanding
temporal-grounding
hierarchical-annotations
post-training
License:
File size: 5,127 Bytes
0ce520b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | """Planned Parquet schema and a synthetic local Hugging Face compatibility check."""
import argparse
import hashlib
import json
from pathlib import Path
from datasets import Dataset, Features, Value
try:
from datasets import List
except ImportError:
# datasets 3.x represents an Arrow list with a one-element Python list.
# Sequence(dict) would instead transpose a list of records into records of lists.
def List(feature):
return [feature]
def features():
text, number = Value("string"), Value("float64")
media = {
"media_id": text, "source_dataset": text, "source_video_id": text,
"kind": text, "video_path": text, "frame_keys": List(text),
"source_frame_indices": List(Value("int64")),
"source_timestamps_sec": List(number), "presentation_timestamps_sec": List(number),
"clip_start_sec": number, "clip_end_sec": number, "duration_sec": number,
"source_fps": number, "effective_fps": number, "ordering": text,
}
return Features({
"row_id": text, "sample_id": text, "source_sample_id": text,
"dataset_version": text, "source_dataset": text, "source_video_ids": List(text),
"task_family": text, "problem_type": text, "data_type": text, "level": Value("int8"),
"prompt_mode": text, "reasoning_tag": text, "parent_sample_id": text,
"parent_dataset_revision": text, "prompt": text,
"messages": List({"role": text, "content": text}),
"answer": text, "answer_format": text, "answer_time_coordinate": text,
"media": List(media), "split_role": text, "mix_id": text,
"mix_row_index": Value("int64"), "annotation_id": text, "build_id": text,
"license_ids": List(text), "prompt_sha256": text, "answer_sha256": text,
"training_metadata_json": text,
})
def fixture_row(row_number=0):
prompt = "<video>\nChoose A or B. Provide your answer inside <answer></answer> tags."
row = dict.fromkeys(features())
row.update({
"row_id": f"fixture_row_{row_number}", "sample_id": f"fixture_sample_{row_number}",
"dataset_version": "synthetic_schema_fixture", "source_dataset": "synthetic",
"source_video_ids": ["fixture_video"], "task_family": "mcq", "problem_type": "llava_mcq", "data_type": "video",
"prompt_mode": "no_cot", "prompt": prompt,
"messages": [{"role": "user", "content": prompt}], "answer": "A", "answer_format": "choice_letter",
"media": [{
"media_id": "fixture_sequence", "source_dataset": "synthetic", "source_video_id": "fixture_video",
"kind": "frames", "video_path": None,
"frame_keys": ["fixture/video/000001.jpg", "fixture/video/000002.jpg"],
"source_frame_indices": [1, 2], "source_timestamps_sec": [0.0, 0.5],
"presentation_timestamps_sec": [0.0, 0.5], "clip_start_sec": 0.0, "clip_end_sec": 1.0,
"duration_sec": 1.0, "source_fps": 2.0, "effective_fps": 2.0, "ordering": "forward",
}],
"split_role": "train", "mix_id": "fixture", "mix_row_index": row_number,
"build_id": "synthetic", "license_ids": ["synthetic_fixture"],
"prompt_sha256": hashlib.sha256(prompt.encode()).hexdigest(),
"answer_sha256": hashlib.sha256(b"A").hexdigest(), "training_metadata_json": "{}",
})
return row
def write_fixture(destination):
destination = Path(destination).resolve()
if destination.exists():
raise ValueError("Fixture directory exists; choose a fresh path")
directory = destination / "data/paper_mt_opd"
directory.mkdir(parents=True)
train = Dataset.from_list([fixture_row(0), fixture_row(1)], features=features())
monitor = fixture_row(2)
monitor["split_role"] = "monitor_val"
Dataset.from_list([monitor], features=features()).to_parquet(str(directory / "monitor_val-00000-of-00001.parquet"))
train.to_parquet(str(directory / "train-00000-of-00001.parquet"))
card = """---
pretty_name: EventLens Synthetic Schema Fixture
configs:
- config_name: paper_mt_opd
default: true
data_files:
- split: train
path: data/paper_mt_opd/train-*.parquet
- split: monitor_val
path: data/paper_mt_opd/monitor_val-*.parquet
---
# Synthetic schema fixture
Three synthetic rows; no paper data or real media is included.
Frame references intentionally have no JPEG payload. This checks Parquet
schema, nested fields, and local dataset-card config/split loading only.
"""
(destination / "README.md").write_text(card)
return destination
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--schema-output", type=Path)
parser.add_argument("--fixture-dir", type=Path)
args = parser.parse_args()
if args.schema_output:
args.schema_output.write_text(json.dumps(features().to_dict(), indent=2) + "\n")
if args.fixture_dir:
print(write_fixture(args.fixture_dir))
if not args.schema_output and not args.fixture_dir:
print(json.dumps({"columns": len(features()), "schema": "eventlens_parquet_v1"}))
if __name__ == "__main__":
main()
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