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README.md

@@ -1,84 +1,127 @@
 ---
-dataset_info:
-- config_name: dpo
-  features:
-  - name: prompt
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  - name: chosen
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  - name: rejected
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  - name: chosen_engagement
-    dtype: float64
-  - name: rejected_engagement
-    dtype: float64
-  - name: chosen_video_id
-    dtype: string
-  - name: rejected_video_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 27078607
-    num_examples: 11607
-  - name: test
-    num_bytes: 1443587
-    num_examples: 611
-  download_size: 28397635
-  dataset_size: 28522194
-- config_name: sft
-  features:
-  - name: messages
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  - name: tier
-    dtype: string
-  - name: engagement_rate
-    dtype: float64
-  - name: like_count
-    dtype: int64
-  - name: play_count
-    dtype: int64
-  - name: duration_seconds
-    dtype: float64
-  - name: video_id
-    dtype: string
-  - name: author
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 32379287
-    num_examples: 23216
-  - name: test
-    num_bytes: 1712781
-    num_examples: 1222
-  download_size: 33177364
-  dataset_size: 34092068
-configs:
-- config_name: dpo
-  data_files:
-  - split: train
-    path: dpo/train-*
-  - split: test
-    path: dpo/test-*
-- config_name: sft
-  data_files:
-  - split: train
-    path: sft/train-*
-  - split: test
-    path: sft/test-*
+license: apache-2.0
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - comedy
+  - stand-up
+  - humor
+  - dpo
+  - sft
+  - funny
+size_categories:
+  - 10K<n<100K
 ---
+
+# FunnyBench: Stand-Up Comedy Dataset for LLM Training
+
+A curated dataset of **24,438 stand-up comedy transcripts** with engagement metrics, designed for teaching LLMs to generate funny content.
+
+## 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`
+
+```python
+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`
+
+```python
+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
+
+```json
+{
+  "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
+
+```json
+{
+  "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}
+}
+```