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: %93.55
  • Accuracy: %93.10

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
3
Safetensors
Model size
11.9M params
Tensor type
F32
·
Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Dataset used to train anilguven/albert_tr_turkish_spam_email

Space using anilguven/albert_tr_turkish_spam_email 1