Fine-tuned DistilBERT for NSFW Inappropriate Text Classification

Model Description

DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW).

The model is a fine-tuned version of DistilBERT.

It was fine-tuned on 19604 Reddit posts pulled from the Comprehensive Abusiveness Detection Dataset.

How to Use

from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="michellejieli/inappropriate_text_classifier")
classifier("I see you’ve set aside this special time to humiliate yourself in public.")
Output:
[{'label': 'NSFW', 'score': 0.9684491753578186}]

Contact

Please reach out to [email protected] if you have any questions or feedback.

Reference

Hoyun Song, Soo Hyun Ryu, Huije Lee, and Jong Park. 2021. A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from Reddit. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 552–561, Online. Association for Computational Linguistics.

Downloads last month
2,044
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Spaces using michellejieli/inappropriate_text_classifier 3