# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("VitalContribution/JokeDetectBERT")
model = AutoModelForSequenceClassification.from_pretrained("VitalContribution/JokeDetectBERT")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VitalContribution/JokeDetectBERT")