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README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-base
+pipeline_tag: token-classification
+tags:
+  - token classification
+  - hallucination detection
+  - transformers
+---
+
+# LettuceDetect: Hallucination Detection Model
+
+**Model Name:** lettucedect-base-modernbert-en-v1  
+**Organization:** KRLabsOrg  
+
+## Overview
+
+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.
+
+## Model Details
+
+- **Architecture:** ModernBERT (Base) with extended context support (up to 8192 tokens)
+- **Task:** Token Classification / Hallucination Detection
+- **Training Dataset:** RagTruth (with potential extensions to biomedical datasets)
+- **Language:** English
+
+## How It Works
+
+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.
+
+## Usage
+
+### Installation
+
+Install the 'lettucedetect' repository