Datasets:

lwaekfjlk
/

ctm-affective

license: other
license_name: research-use-only
license_link: LICENSE
language:
  - en
task_categories:
  - video-classification
  - audio-classification
  - text-classification
tags:
  - multimodal
  - sarcasm-detection
  - humor-detection
  - mustard
  - ur-funny
  - affective-computing
size_categories:
  - 1K<n<10K

CTM Affective Benchmarks: MUStARD + UR-FUNNY

Preprocessed multimodal media and text splits for the two affective-reasoning benchmarks used in the ctm-ai exp_affective experiments:

MUStARD — multimodal sarcasm detection from TV sitcom clips (Friends, The Big Bang Theory, The Golden Girls, Sarcasmaholics).
UR-FUNNY — multimodal humor detection from TED talk clips.

This repo bundles the derived media (raw clips, muted video streams, and extracted audio) alongside the JSON text splits, so an experiment run can fetch everything from one place.

Repository layout

mustard/
  mmsd_raw_data/utterances_final/   690 mp4  — raw clips (video + audio)
  mustard_muted_videos/             690 mp4  — video-only (audio stream removed)
  mustard_audios/                   356 mp4  — audio-only, test split only
  mustard_dataset/                    9 json — test split + evaluation subsets
  mustard_smoke3.json                        — 3-example smoke test

urfunny/
  urfunny_videos/                   992 mp4  — raw clips (video + audio)
  urfunny_muted_videos/             992 mp4  — video-only (audio stream removed)
  urfunny_audios/                   992 mp4  — audio-only
  data_raw/                           5 json — test split + evaluation subsets
  urfunny_smoke3.json                        — 3-example smoke test

Total: 4,728 files, ~4.55 GB.

Naming conventions

MUStARD clip ids look like 1_10004 (non-sarcastic pool) and 2_223 (sarcastic pool). Audio files add an _audio suffix: 2_223_audio.mp4.
UR-FUNNY clip ids are plain integers (1008), with audio also suffixed _audio (1008_audio.mp4).
Media files carry the .mp4 container throughout, including the audio-only tracks — they are audio streams in an mp4 container, not video.

Coverage note

mustard_audios/ contains 356 files, not 690: audio was extracted only for the clips in mustard_dataset/mustard_dataset_test.json (the 356-example test split). Those 356 ids are a strict subset of the 690 ids in mustard_muted_videos/ and mmsd_raw_data/utterances_final/. UR-FUNNY has full 992/992/992 coverage across all three modality folders.

Text split schemas

mustard/mustard_dataset/mustard_dataset_test.json — a dict keyed by clip id (356 entries):

field	description
`utterance`	the target utterance text
`speaker`	speaker of the target utterance
`context`	list of preceding utterances
`context_speakers`	speakers for each context utterance
`show`	source sitcom
`sarcasm`	boolean label

urfunny/data_raw/urfunny_dataset_test.json — a dict keyed by clip id (992 entries):

field	description
`context_sentences`	list of setup sentences
`punchline_sentence`	the punchline
`label`	humor label

The remaining JSON files in each folder are evaluation subsets (*_subset_5/6/20/100.json) and retry/missing-id lists used for partial reruns.

Usage

from huggingface_hub import snapshot_download

# everything (~4.55 GB)
snapshot_download("lwaekfjlk/ctm-affective", repo_type="dataset")

# just the MUStARD text splits
snapshot_download(
    "lwaekfjlk/ctm-affective",
    repo_type="dataset",
    allow_patterns="mustard/mustard_dataset/*",
)

# just UR-FUNNY audio
snapshot_download(
    "lwaekfjlk/ctm-affective",
    repo_type="dataset",
    allow_patterns="urfunny/urfunny_audios/*",
)

There is intentionally no configs: block in the card metadata: the JSON files are id-keyed dicts rather than record lists, so the dataset viewer would not parse them. Load them with json.load directly.

Provenance and licensing

The clips are derived from third-party copyrighted footage (TV sitcom episodes for MUStARD, TED talks for UR-FUNNY) and are redistributed here only as preprocessed research artifacts. Use is intended for non-commercial academic research. Rights to the underlying footage remain with their original owners; consult the upstream datasets for their terms before redistributing.

Please cite the original datasets:

@inproceedings{castro2019towards,
  title     = {Towards Multimodal Sarcasm Detection (An {\_}Obviously{\_} Perfect Paper)},
  author    = {Castro, Santiago and Hazarika, Devamanyu and P{\'e}rez-Rosas, Ver{\'o}nica
               and Zimmermann, Roger and Mihalcea, Rada and Poria, Soujanya},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for
               Computational Linguistics (ACL)},
  year      = {2019}
}

@inproceedings{hasan2019urfunny,
  title     = {{UR-FUNNY}: A Multimodal Language Dataset for Understanding Humor},
  author    = {Hasan, Md Kamrul and Rahman, Wasifur and Zadeh, Amir and Zhong, Jianyuan
               and Tanveer, Md Iftekhar and Morency, Louis-Philippe and Hoque, Mohammed (Ehsan)},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural
               Language Processing (EMNLP)},
  year      = {2019}
}