lettucedect base modernbert en v1 EU AI Act Compliance Profile

LettuceDetect: Hallucination Detection Model

Model Name: lettucedect-base-modernbert-en-v1
Organization: KRLabsOrg
Github: https://github.com/KRLabsOrg/LettuceDetect

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.

This is our Large model based on ModernBERT-large

Model Details

• Architecture: ModernBERT (Large) with extended context support (up to 8192 tokens)
• Task: Token Classification / Hallucination Detection
• Training Dataset: RagTruth
• 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

pip install lettucedetect

Using the model

from lettucedetect.models.inference import HallucinationDetector

# For a transformer-based approach:
detector = HallucinationDetector(
    method="transformer", model_path="KRLabsOrg/lettucedect-base-modernbert-en-v1"
)

contexts = ["France is a country in Europe. The capital of France is Paris. The population of France is 67 million.",]
question = "What is the capital of France? What is the population

Your obligations as a lettucedect base modernbert en v1 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library

Base Model