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MikhailRepkin
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news_classifier

Text Classification
Transformers
Safetensors
Russian
bert
Inference Endpoints
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Model Details

Model Description

News_classifier is a fine-tuned model designed for binary classifying (news/not news) from various Russian-language Telegram channels. This model can be integrated into a news aggregation service.

  • Model type: Sentence RuBERT (Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters)
  • Language(s): russian (ru)
  • License: mit
  • Finetuned from model: DeepPavlov/rubert-base-cased-sentence

Dataset

  • Russian telegram posts
  • train/valid/test: 2970/165/165

Training Details

  • token max length: 512
  • num labels: 2
  • batch size: 16
  • learning rate: 2e-5
  • train epochs: 20
  • weight decay: 0.01

Metrics:

  • Matthews_correlation (training evaluation metric): 0.89
  • Accuracy: 0.95

Label Scheme

  • LABEL_1 - news
  • LABEL_0 - not news
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