readme: ---
  license: creativeml-openrail-m
  language: "en"
  tags:
  - distilroberta
  - sentiment
  - NSFW
  - inappropriate 
  - spam
  - twitter
  - reddit
  
  widget:
  - text: "I like you. You remind me of me when I was young and stupid."
  - text: "I see you’ve set aside this special time to humiliate yourself in public."
  - text: "Have a great weekend! See you next week!"
  
  ---
  
  # 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](https://huggingface.co/docs/transformers/model_doc/distilbert).
  
  It was fine-tuned on 19604 Reddit posts pulled from the [Comprehensive Abusiveness Detection Dataset](https://aclanthology.org/2021.conll-1.43/).
  
  # How to Use 
  
  ```python
  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.")
  ```
  
  ```python
  Output:
  [{'label': 'NSFW', 'score': 0.9684491753578186}]
  ```
  
  # Contact
  
  Please reach out to [michelle.li851@duke.edu](mailto:michelle.li851@duke.edu) 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.
  ```
  ---
  