Principle:AnswerDotAI RAGatouille Trainer Initialization

Knowledge Sources	ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction RAGatouille
Domains	NLP, Information_Retrieval, Training
Last Updated	2026-02-12 12:00 GMT

Overview

An initialization mechanism that configures a ColBERT training pipeline from a base pretrained model, preparing the system for data ingestion and model fine-tuning.

Description

Trainer Initialization sets up the training infrastructure for fine-tuning or training a ColBERT model. It loads a base model (either an existing ColBERT checkpoint or a BERT/RoBERTa-like model) in training mode, which skips loading the inference checkpoint to save memory. The trainer stores the model name for checkpoint naming, configures GPU allocation, and initializes empty containers for the document collection and training triplets.

Key setup includes:

• Loading the base model in training_mode=True (no inference checkpoint)
• Storing the model name for checkpoint directory naming
• Recording the language code for hard negative miner model selection
• Initializing empty collection, queries, and training_triplets containers

Usage

Use this principle as the first step in any ColBERT training workflow. This is required before:

• Preparing training data with hard negative mining
• Launching model fine-tuning
• Training a new ColBERT model from a BERT backbone

Theoretical Basis

ColBERT training uses a contrastive learning objective over triplets of (query, positive passage, negative passage). The model learns to produce token embeddings where positive query-document pairs have higher MaxSim scores than negative pairs:

${ℒ}=-\log \frac{e^{S(q,d^{+})}}{e^{S(q,d^{+})}+\sum _{d^{-}\in N}{e}^{S(q,d^{-})}}$

Where S is the MaxSim scoring function and N is the set of negative passages. Initialization prepares the model weights that will be optimized by this objective.

Related Pages

Implemented By

• Implementation:AnswerDotAI_RAGatouille_RAGTrainer_Init