rut5_base_sum_gazeta

Performs abstractive summarization of Russian-language documents using a fine-tuned RuT5-base encoder-decoder transformer model trained on the Gazeta news corpus. The model uses a sequence-to-sequence approach where the input text is tokenized and encoded into contextual embeddings, then decoded to generate a compressed summary that may contain tokens not present in the source. Fine-tuning on domain-specific news data enables it to preserve journalistic structure and key information while reducing length.

Supports deployment via HuggingFace's optimized Text Generation Inference (TGI) server, which provides batching, dynamic padding, and quantization support for efficient multi-request processing. The model can be served as a REST API endpoint with automatic request batching, allowing multiple summarization requests to be processed together in a single forward pass, reducing per-request latency overhead and improving throughput for production workloads.

The model is compatible with HuggingFace Endpoints and Azure deployment platforms, enabling one-click deployment to managed inference services without custom infrastructure. This compatibility means the model weights, tokenizer configuration, and inference code are pre-optimized for these platforms' inference runtimes, allowing developers to deploy directly from the HuggingFace model hub with minimal configuration.

Uses the T5 encoder-decoder architecture with multi-head self-attention mechanisms that learn to weight important tokens and phrases in the input text. The encoder processes the full input document and creates contextual representations where each token attends to all other tokens, enabling the model to identify and preserve key information (named entities, dates, numbers) while compressing less critical content. The decoder then generates the summary token-by-token, using cross-attention to focus on relevant encoder outputs.

Released under Apache 2.0 license with full model weights, tokenizer, and configuration files publicly available on HuggingFace Hub. The model can be downloaded, modified, fine-tuned, and deployed without licensing restrictions or commercial use limitations. Training was performed on the publicly available Gazeta news dataset, enabling reproducibility and community contributions to improve the model.