MikhailRepkin
/

news_classifier

@@ -8,201 +8,38 @@ pipeline_tag: text-classification
 widget:
 - text: "Взрыв газа произошел в 2-этажном доме в поселке под Казанью, пострадали четыре человека, сообщает МЧС"
-  example_title: "новость"
 - text: "Сын поздравил меня с днём рождения стихами ❤️"
-  example_title: "не новость"
 ---
-# Model Card for Model ID
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 widget:
 - text: "Взрыв газа произошел в 2-этажном доме в поселке под Казанью, пострадали четыре человека, сообщает МЧС"
+  example_title: "Новость"
 - text: "Сын поздравил меня с днём рождения стихами ❤️"
+  example_title: "Не новость"
 ---
 ## Model Details
 ### Model Description
+News_classifier is a fine-tuned model designed for classifying news posts 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) (NLP):** 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