anilguven/distilbert_tr_turkish_spam_email

Model Info

This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.

• —LABEL_0: ham/normal mail
• —LABEL_1: spam mail

Model Sources

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• —Dataset: https://huggingface.co/datasets/anilguven/turkishspamemail
• —Paper: https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
• —Demo-Coding [optional]: https://github.com/anil1055/Turkishspamemaildetectionwithlanguagemodels
• —Finetuned from model [optional]: https://huggingface.co/dbmdz/distilbert-base-turkish-cased

Preprocessing

You must apply removing stopwords, stemming, or lemmatization process for Turkish.

Model Load safetensors

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Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors

Results

• —F1-score: %94.00
• —Accuracy: %93.60

Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }

APA:

GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.