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import json |
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import os |
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import datasets |
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_DESCRIPTION = """ |
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SPLICE is a human-curated benchmark designed to evaluate the temporal and causal reasoning |
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capabilities of Multimodal Large Language Models (MLLMs). The core task is to reorder a set of |
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shuffled video segments from a single procedural event into their correct chronological sequence. |
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The dataset is derived from 3,381 instructional videos from the COIN dataset, segmented into |
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11,423 coherent event clips. |
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""" |
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_CITATION = """ |
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@inproceedings{ |
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ballout2025can, |
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title={{Can you {SPLICE} it together? A Human Curated Benchmark for Probing Visual Reasoning in {VLM}s}}, |
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author={Mohamad Ballout* and Okajevo Wilfred* and Seyedalireza Yaghoubi and Nohayr Muhammad Abdelmoneim and Julius Mayer and Elia Bruni}, |
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booktitle={The 2025 Conference on Empirical Methods in Natural Language Processing}, |
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year={2025}, |
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url={https://openreview.net/forum?id=deFgBHsHxl} |
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} |
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""" |
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class SpliceBenchmark(datasets.GeneratorBasedBuilder): |
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"""The SPLICE Benchmark Dataset.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"video_id": datasets.Value("string"), |
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"domain": datasets.Value("string"), |
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"class": datasets.Value("string"), |
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"subset": datasets.Value("string"), |
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"video_url": datasets.Value("string"), |
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"duration": datasets.Value("float"), |
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"segments": datasets.Sequence( |
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{ |
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"part": datasets.Value("int32"), |
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"segment_id": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"start": datasets.Value("float"), |
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"end": datasets.Value("float"), |
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"video_clip": datasets.Video() |
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} |
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), |
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} |
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), |
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homepage="https://huggingface.co/datasets/prokajevo/splice-benchmark", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(".") |
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metadata_path = os.path.join(data_dir, "splice_segment_metadata.json") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"metadata_path": metadata_path, "data_dir": data_dir}, |
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), |
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] |
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def _generate_examples(self, metadata_path, data_dir): |
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with open(metadata_path, "r") as f: |
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data = json.load(f) |
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video_count = 0 |
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for video_info in data: |
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try: |
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if not video_info.get("segments"): |
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continue |
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segments_data = [] |
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for segment in video_info.get("segments", []): |
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if "output_path" in segment and segment["output_path"]: |
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video_path = os.path.join(data_dir, segment["output_path"]) |
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if os.path.exists(video_path): |
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segments_data.append({ |
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"part": segment.get("part", -1), |
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"segment_id": segment.get("segment_id", ""), |
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"label": segment.get("label", ""), |
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"start": segment.get("start", -1.0), |
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"end": segment.get("end", -1.0), |
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"video_clip": video_path, |
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}) |
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if segments_data: |
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yield video_count, { |
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"video_id": video_info.get("video_id", ""), |
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"domain": video_info.get("Domain", ""), |
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"class": video_info.get("class", ""), |
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"subset": video_info.get("subset", ""), |
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"video_url": video_info.get("video_url", ""), |
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"duration": video_info.get("duration", -1.0), |
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"segments": segments_data, |
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} |
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video_count += 1 |
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except Exception as e: |
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print(f"--> WARNING: Skipping corrupted data for video {video_info.get('video_id', 'unknown')}. Error: {e}") |
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continue |
