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This is the **merged** version of the mmBERT-32K factcheck classifier model, ready for direct inference without PEFT. - **Base Model**: vllm-sr/mmbert-32k-yarn - **Task**: Text Classification - **Number of Labels**: 2 - **Context Length**: 32,768 tokens - **Architecture**: ModernBERT with YaRN RoPE scaling - **LoRA Adapter**: vllm-sr/mmbert32k-factcheck-classifier-lora - **Base Model**: vllm-sr/mmbert-32k-yarn

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Full merged model for intent classification based on mmBERT-32K-YaRN (32K context, multilingual). This is the LoRA adapter merged with the base model for direct inference without PEFT. biology, business, chemistry, computer science, economics, engineering, health, history, law, math, other, philosophy, physics, psychology This merged model is compatible with the candle-binding Rust library for high-performance inference in production systems.

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Vela Domain identifies the subject of a request across 14 domains to help select relevant models and tools. Macro F1 (×100) on the same development sets, compared with the original mmBERT32K Domain model. Higher is better. Short requests cover English, Chinese, Spanish, French, German and Japanese. Long-context evaluation uses 28 English/Chinese examples extended to each length. Both models process the complete input in FP32. These development sets informed Vela checkpoint selection; they are not an independent test set.

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- **Language model:** allegro/herbert-base-cased - **Language:** pl - **Training data:** Reviews + own data - **Blog post:** Sentiment analysis - COVID-19 – the source of the heated discussion

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