| 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 |
