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--- |
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license: mit |
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language: |
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- ru |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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widget: |
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- text: "Взрыв газа произошел в 2-этажном доме в поселке под Казанью, пострадали четыре человека, сообщает МЧС" |
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example_title: "Новость" |
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- text: "Сын поздравил меня с днём рождения стихами ❤️" |
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example_title: "Не новость" |
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--- |
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## Model Details |
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### Model Description |
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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. |
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- **Model type:** Sentence RuBERT (Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters) |
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- **Language(s):** russian (ru) |
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- **License:** mit |
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- **Finetuned from model:** `DeepPavlov/rubert-base-cased-sentence` |
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## Dataset |
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- Russian telegram posts |
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- train/valid/test: 2970/165/165 |
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## Training Details |
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- token max length: 512 |
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- num labels: 2 |
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- batch size: 16 |
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- learning rate: 2e-5 |
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- train epochs: 20 |
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- weight decay: 0.01 |
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## Metrics: |
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- Matthews_correlation (training evaluation metric): 0.89 |
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- Accuracy: 0.95 |
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## Label Scheme |
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- LABEL_1 - news |
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- LABEL_0 - not news |
