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name: reranking description: "Train and run Tevatron cross-encoder rerankers, prepare pairwise rerank inputs, and interpret reranker score outputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tevatron Reranking

Use this sub-skill when the task is about Tevatron second-stage cross-encoder reranking: training a reranker from query/positive/negative groups, turning first-stage runs into pairwise JSONL, running reranker inference, using RankLLaMA-style LoRA checkpoints, or interpreting score files.

Start Here

1. Read Reranker Workflows for end-to-end train, inference, rerank-input, and RankLLaMA command plans.
2. Read Arguments Reference for current tevatron.reranker.driver.train and tevatron.reranker.driver.rerank arguments, JSONL fields, grouped logits, and LoRA options.
3. Run prepare_rerank_input.py when you have query JSONL, corpus JSONL, and a first-stage run file and need pairwise reranker input.
4. Read Troubleshooting for pair/triple format mistakes, train_group_size mismatches, LoRA adapter issues, missing optional dependencies, score formatting, and download/cache failures.

Scope Boundaries

This sub-skill covers:

• tevatron.reranker.driver.train cross-encoder reranker training.
• tevatron.reranker.driver.rerank single-device reranker inference.
• Training JSONL fields, inference JSONL fields, grouped-logit loss behavior, LoRA reranker options, and raw score output interpretation.
• Rerank input preparation from first-stage retrieval runs plus local query/corpus fixtures.
• RankLLaMA-style reranking and training patterns, with cross-links to sibling LLM guidance when the task becomes model-family or hardware specific.

Use sibling Tevatron sub-skills instead for:

• First-stage dense/sparse retriever training: training.
• Query/corpus encoding, FAISS search, sharded retrieval, and first-stage ranking files: encoding-retrieval.
• General dataset schema validation and run-format conversion: data-preparation.
• LLaMA/Mistral/Qwen multimodal or LLM retriever hardware/dependency routing beyond reranker-specific flags: multimodal-llm.

Runtime Assumptions

• Tevatron reranker argument dataclasses import in a minimal install, but training and inference require optional torch because dataset, collator, model, trainer, and driver modules use PyTorch.
• transformers and datasets are core Tevatron dependencies; peft is required only for --lora or --lora_name_or_path workflows.
• RankLLaMA/Mistral/LLaMA-style workflows usually require gated model access, GPU memory, compatible precision settings, and optional acceleration packages.
• Keep tiny local JSONL/run fixtures for command validation before launching downloads, GPU jobs, or long training.

Fast Decision Guide

• Need pairwise rerank JSONL: use prepare_rerank_input.py, then verify fields in Arguments Reference.
• Need a BERT-style reranker training plan: use Reranker Workflows.
• Need RankLLaMA or instruction prefixes: use Reranker Workflows.
• Need to diagnose grouped training loss: use Troubleshooting.
• Need to explain query_id<TAB>docid<TAB>score: use Reranker Workflows.