Datasets:
Size:
10K - 100K
License:
| import json | |
| import os | |
| import datasets | |
| _DESCRIPTION = """ | |
| SPLICE is a human-curated benchmark designed to evaluate the temporal and causal reasoning | |
| capabilities of Multimodal Large Language Models (MLLMs). The core task is to reorder a set of | |
| shuffled video segments from a single procedural event into their correct chronological sequence. | |
| The dataset is derived from 3,381 instructional videos from the COIN dataset, segmented into | |
| 11,423 coherent event clips. | |
| """ | |
| _CITATION = """ | |
| @inproceedings{ | |
| ballout2025can, | |
| title={{Can you {SPLICE} it together? A Human Curated Benchmark for Probing Visual Reasoning in {VLM}s}}, | |
| author={Mohamad Ballout* and Okajevo Wilfred* and Seyedalireza Yaghoubi and Nohayr Muhammad Abdelmoneim and Julius Mayer and Elia Bruni}, | |
| booktitle={The 2025 Conference on Empirical Methods in Natural Language Processing}, | |
| year={2025}, | |
| url={https://openreview.net/forum?id=deFgBHsHxl} | |
| } | |
| """ | |
| class SpliceBenchmark(datasets.GeneratorBasedBuilder): | |
| """The SPLICE Benchmark Dataset.""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "video_id": datasets.Value("string"), | |
| "domain": datasets.Value("string"), | |
| "class": datasets.Value("string"), | |
| "subset": datasets.Value("string"), | |
| "video_url": datasets.Value("string"), | |
| "duration": datasets.Value("float"), | |
| "segments": datasets.Sequence( | |
| { | |
| "part": datasets.Value("int32"), | |
| "segment_id": datasets.Value("string"), | |
| "label": datasets.Value("string"), | |
| "start": datasets.Value("float"), | |
| "end": datasets.Value("float"), | |
| "video_clip": datasets.Video() | |
| } | |
| ), | |
| } | |
| ), | |
| homepage="https://huggingface.co/datasets/prokajevo/splice-benchmark", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(".") | |
| metadata_path = os.path.join(data_dir, "splice_segment_metadata.json") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"metadata_path": metadata_path, "data_dir": data_dir}, | |
| ), | |
| ] | |
| def _generate_examples(self, metadata_path, data_dir): | |
| with open(metadata_path, "r") as f: | |
| data = json.load(f) | |
| video_count = 0 | |
| for video_info in data: | |
| try: | |
| if not video_info.get("segments"): | |
| continue | |
| segments_data = [] | |
| for segment in video_info.get("segments", []): | |
| if "output_path" in segment and segment["output_path"]: | |
| video_path = os.path.join(data_dir, segment["output_path"]) | |
| if os.path.exists(video_path): | |
| segments_data.append({ | |
| "part": segment.get("part", -1), | |
| "segment_id": segment.get("segment_id", ""), | |
| "label": segment.get("label", ""), | |
| "start": segment.get("start", -1.0), | |
| "end": segment.get("end", -1.0), | |
| "video_clip": video_path, | |
| }) | |
| if segments_data: | |
| yield video_count, { | |
| "video_id": video_info.get("video_id", ""), | |
| "domain": video_info.get("Domain", ""), | |
| "class": video_info.get("class", ""), | |
| "subset": video_info.get("subset", ""), | |
| "video_url": video_info.get("video_url", ""), | |
| "duration": video_info.get("duration", -1.0), | |
| "segments": segments_data, | |
| } | |
| video_count += 1 | |
| except Exception as e: | |
| print(f"--> WARNING: Skipping corrupted data for video {video_info.get('video_id', 'unknown')}. Error: {e}") | |
| continue |