AI가 움직이고 미래가 업데이트됩니다.lettucedect-large-modernbert-en-v1
**Model Name:** lettucedect-large-modernbert-en-v1 **Organization:** KRLabsOrg **Github:** https://github.com/KRLabsOrg/LettuceDetect LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for Retrieval-Augmented Generation (RAG) applications. This model is built on **ModernBERT**, which has been specifically chosen and trained becasue of its extended context support (up to **8192 tokens**). This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context. The model is trained to identify tokens in the answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.
번역 준비 중입니다. 원문을 표시합니다.
**Model Name:** lettucedect-large-modernbert-en-v1 **Organization:** KRLabsOrg **Github:** https://github.com/KRLabsOrg/LettuceDetect LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for Retrieval-Augmented Generation (RAG) applications. This model is built on **ModernBERT**, which has been specifically chosen and trained becasue of its extended context support (up to **8192 tokens**). This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context. The model is trained to identify tokens in the answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.