---
library_name: transformers
tags: []
---
# 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
```python
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
