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

Preprocessing

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

Model Load safetensors

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

Results

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

Citation

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.

Downloads last month
18
Safetensors
Model size
68.1M params
Tensor type
F32
·
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.

Dataset used to train anilguven/distilbert_tr_turkish_spam_email

Space using anilguven/distilbert_tr_turkish_spam_email 1