# humor-no-humor Open-source Humor Detection Model - Free Deployment, Efficient and Precise Identification of Humorous Content

Humor No Humor

Developed by mohameddhiab

A humor detection model fine-tuned based on distilbert-base-uncased, achieving an F1 score of 0.9537 on the evaluation set

Downloads 351

Release Time : 6/22/2023

Model Overview

This model is used to identify whether text has humorous attributes, suitable for scenarios such as content moderation and social media analysis

High-Precision Humor Detection

Achieves an F1 score of 0.9537 on the evaluation set, demonstrating excellent humor recognition capabilities

Lightweight Model

Based on the DistilBERT architecture, more lightweight and efficient compared to the original BERT model

Easy to Use

Can be directly applied to text classification tasks without complex preprocessing

Text Classification

Humor Detection

Natural Language Understanding

Content Moderation

Social Media Content Filtering

Automatically identifies humorous content posted by users

Improves content classification efficiency

User Experience Optimization

Chatbot Response Optimization

Recognizes humorous intent in user input

Provides a more human-like interaction experience

Training Loss	Epoch	Step	Validation Loss	F1
0.1707	1.0	1677	0.1398	0.9423
0.1427	2.0	3354	0.1291	0.9531
0.1384	3.0	5031	0.1269	0.9537

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