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--- |
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license: creativeml-openrail-m |
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language: "en" |
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tags: |
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- distilroberta |
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- sentiment |
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- NSFW |
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- inappropriate |
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- spam |
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- twitter |
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- reddit |
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widget: |
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- text: "I like you. You remind me of me when I was young and stupid." |
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- text: "I see you’ve set aside this special time to humiliate yourself in public." |
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- text: "Have a great weekend! See you next week!" |
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--- |
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# Fine-tuned DistilBERT for NSFW Inappropriate Text Classification |
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# Model Description |
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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). |
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The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert). |
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It was fine-tuned on 19604 Reddit posts pulled from the [Comprehensive Abusiveness Detection Dataset](https://aclanthology.org/2021.conll-1.43/). |
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# How to Use |
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```python |
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from transformers import pipeline |
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classifier = pipeline("sentiment-analysis", model="michellejieli/inappropriate_text_classifier") |
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classifier("I see you’ve set aside this special time to humiliate yourself in public.") |
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``` |
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```python |
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Output: |
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[{'label': 'NSFW', 'score': 0.9684491753578186}] |
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``` |
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# Contact |
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Please reach out to [michelle.li851@duke.edu](mailto:michelle.li851@duke.edu) if you have any questions or feedback. |
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# Reference |
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``` |
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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. |
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``` |
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--- |
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