FRED T5 Summarizer
A Russian text summarization model developed by SberDevices, based on the T5 architecture with 1.7B parameters
Downloads 11.76k
Release Time : 4/2/2024
Model Overview
This model is specifically designed for Russian text summarization tasks, capable of compressing long texts into concise summaries. Trained on a mixed open-source summarization dataset, it uses the prefix token '<LM>' for conditional generation.
Model Features
Russian language optimization
Specially trained and optimized for Russian text, excelling in Russian summarization tasks
Large model capacity
1.7B parameter scale, capable of capturing complex semantic relationships in text
Conditional generation
Uses the prefix token '<LM>' to control the generation process, improving summary quality
Model Capabilities
Russian text summarization
Long text compression
Key information extraction
Use Cases
Content summarization
News summarization
Compress lengthy news reports into concise summaries
Outputs brief news summaries containing key information
Document summarization
Extract core content from long documents
Generates document summaries retaining main points
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