metadata
license: cc0-1.0
language:
- en
- fr
- tr
tags:
- art
- emoji
- brainrot
- text-generation
pretty_name: DistilGPT2 fine-tuned on YouTube Shorts comments
size_categories:
- 10M<n<100M
datasets:
- PingVortex/Youtube_shorts_comments
base_model:
- distilbert/distilgpt2
pipeline_tag: text-generation
library_name: transformers
Youtube shorts comment generator 🧠 (I couldn't come up with a more original name)
A fine-tuned DistilGPT2 model trained on 1.4M+ YouTube Shorts comments - the perfect language model for generating cursed internet humor, emoji spam, and authentic YouTube degeneracy.
- Base model: distilgpt2
- Trained on: YouTube Shorts Comments Dataset
- Creator: PingVortex
Model Details 🔥
- Parameters: 82M (DistilGPT2 architecture)
- Training Data: 1,475,500 YouTube Shorts comments
- Special Skills: Emoji generation, broken English, random character generation
Usage Example 🐍
from transformers import pipeline
brainrot = pipeline('text-generation', model='PingVortex/Youtube-shorts-comment-generator')
output = brainrot("When you see a Sigma edit:", max_length=50)
print(output[0]['generated_text'])
Sample output:"When you see a Sigma edit: 😂😂😂😂 The white one on the last pic?😂😂😂😅😅😅😊😊😊😅😮😮😅"
Training Info ⚙️
- Epochs: 1
- Batch Size: 8
- Hardware: Google Colab T4 GPU
- Training Time: ~2 hours
- Loss: 0.24
Ethical Considerations ⚠️
This model may generate:
- Extreme emoji spam (🔥💀🤣)
- Nonsensical combinations
- Mild brain damage
- Occasional coherent text
Use responsibly (or irresponsibly, we don't judge).
License 📜
CC0 1.0 Universal (Public Domain)
Go nuts - no restrictions