VitalContribution/JokeDetectBERT

Model Card: DistilBERT-based Joke Detection (needed this because I'm German)

Model Details

• Model Type: Fine-tuned DistilBERT base model (uncased)
• Task: Binary classification for joke detection
• Output: Joke or No-joke sentiment

Training Data

• Dataset: 200k Short Texts for Humor Detection
• Link: https://www.kaggle.com/datasets/deepcontractor/200k-short-texts-for-humor-detection
• Size: 200,000 labeled short texts
• Distribution: Equally balanced between humor and non-humor
• Source: Primarily from r/jokes and r/cleanjokes subreddits

Base Model

DistilBERT base model (uncased), a distilled version of BERT optimized for efficiency while maintaining performance.

Usage

from transformers import pipeline

model_id = "VitalContribution/JokeDetectBERT"
pipe = pipeline('text-classification', model=model_id)

joke_questionmark = "What do elves learn in school? The elf-abet."

out = pipe(joke_questionmark)[0]
label = out['label']
confidence = out['score']
result = "JOKE" if label == 'LABEL_1' else "NO JOKE"
print(f"Prediction: {result} ({confidence:.2f})")

Training Details

Parameter	Value
Model	DistilBERT (base-uncased)
Task	Sequence Classification
Number of Classes	2
Batch Size	32 (per device)
Learning Rate	2e-4
Weight Decay	0.01
Epochs	2
Warmup Steps	100
Best Model Selection	Based on eval_loss

Model Evaluation