---
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.
```
---