Datasets:

fchaubard
/

funny_bench

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - comedy
  - stand-up
  - humor
  - dpo
  - sft
  - funny
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: text
      dtype: string
    - name: source
      dtype: string
  splits:
    - name: train
      num_bytes: 64671620
      num_examples: 369940
    - name: test
      num_bytes: 1321826
      num_examples: 7550
  download_size: 37816655
  dataset_size: 65993446
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

FunnyBench: Stand-Up Comedy Dataset for LLM Training

LLMs aren't funny. This initiative tries to solve that. A curated dataset of 24,438 stand-up comedy transcripts with engagement metrics, designed for teaching LLMs to generate funny content. Shout out to Shofo (https://www.shofo.ai/) for providing the dataset for free for public use!

Dataset Splits

SFT (Supervised Fine-Tuning)

23,216 train / 1,222 test examples
Chat format with quality-tier conditioning
Fields: messages, tier, engagement_rate, like_count, play_count, duration_seconds, video_id, author

from datasets import load_dataset
ds = load_dataset("fchaubard/funny_bench", "sft")

DPO (Direct Preference Optimization)

11,607 train / 611 test preference pairs
Pairs matched by duration bucket
"Chosen" = higher engagement rate, "Rejected" = lower engagement rate
Fields: prompt, chosen, rejected, chosen_engagement, rejected_engagement

from datasets import load_dataset
ds = load_dataset("fchaubard/funny_bench", "dpo")

Source Data

29,729 TikTok stand-up comedy clips (pre-filtered: 10,000+ likes, English, standup hashtags)
Transcribed using NVIDIA Canary-Qwen 2.5B
Speaker diarization via NVIDIA NeMo MSDD
Labels: [COMEDIAN], [AUDIENCE], [LAUGHTER]

Cleaning Pipeline

Filter	Threshold	Dropped
Transcript length	80-12,000 chars	1,163
Duration	15-300 seconds	2,098
Word repetition score	<= 0.55	1,954
Unique word count	>= 15	54
ASR garbage detection	trigram loops	22
Total removed		5,291 (17.8%)
Clean dataset		24,438 (82.2%)

Quality Tiers (SFT)

Each SFT example has a quality tier based on engagement rate (likes/views):

Tier	Percentile	Engagement Rate	Count
`[LEGENDARY]`	Top 5%	> 21.8%	1,222
`[KILLER]`	75-95th	14.7-21.8%	4,888
`[SOLID]`	50-75th	10.8-14.7%	6,109
`[WARMING_UP]`	Bottom 50%	< 10.8%	12,219

At inference, prompt with [LEGENDARY] to generate top-tier comedy.

Why Engagement Rate?

Raw like counts are dominated by virality and follower counts. The engagement rate (likes/views) better captures per-viewer funniness. A clip with 1M views and 200K likes (20%) is funnier per-viewer than one with 100M views and 5M likes (5%).

SFT Format

{
  "messages": [
    {"role": "system", "content": "You are a stand-up comedian performing a live set..."},
    {"role": "user", "content": "[LEGENDARY] Perform a stand-up comedy bit."},
    {"role": "assistant", "content": "[COMEDIAN]: So where are you from?\n[AUDIENCE]: Texas!\n[COMEDIAN]: Texas? Oh man...\n[LAUGHTER]"}
  ],
  "tier": "LEGENDARY",
  "engagement_rate": 0.22,
  "like_count": 500000,
  "play_count": 2200000
}

DPO Format

{
  "prompt": [
    {"role": "system", "content": "You are a stand-up comedian..."},
    {"role": "user", "content": "Perform a stand-up comedy bit."}
  ],
  "chosen": [{"role": "assistant", "content": "...funnier transcript..."}],
  "rejected": [{"role": "assistant", "content": "...less funny transcript..."}],
  "chosen_engagement": 0.18,
  "rejected_engagement": 0.05
}

Limitations

ASR artifacts from NVIDIA Canary-Qwen 2.5B transcription
Comedy depends heavily on delivery and timing that text can't capture
TikTok bias toward short-form, punchy comedy
Engagement != funny (controversy and relatability also drive engagement)

Citation

If you use this dataset, please cite:

@misc{funnybench2026,
  title={FunnyBench: Teaching LLMs Stand-Up Comedy with Engagement-Based Preference Learning},
  year={2026}
}