What this does

Fine-tuning adapts a pretrained cross-encoder reranker to the vocabulary, phrasing, and relevance criteria specific to your domain. A generic reranker often misjudges domain-specific terminology or conceptual relevance. Fine-tuning on labeled query-document pairs from your own corpus produces a model that ranks your documents far more accurately than any general-purpose alternative.

Steps

Prepare training data. Format each example as (query, document_text, label). Labels should reflect relevance on a scale (e.g., 0–4) or binary relevance. Aim for at least 500 high-quality pairs for meaningful adaptation.
Split the data into training (80%) and validation (20%) sets. Keep query distribution representative of your production traffic.
Configure the training run. Use a low learning rate (2e-5 to 5e-5) and a small batch size to avoid catastrophic forgetting. Set 1–3 epochs; longer training on small datasets causes overfitting.

from sentence_transformers import CrossEncoder, Trainer, TrainingArguments

model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2", max_length=512)
train_data = [{"query": q, "text": d, "label": l} for q, d, l in pairs]

training_args = TrainingArguments(
    output_dir="./domain-reranker",
    learning_rate=3e-5,
    per_device_train_batch_size=8,
    num_train_epochs=2,
    evaluation_strategy="steps",
    eval_steps=100,
)
trainer = Trainer(model=model, args=training_args, train_dataset=train_data)
trainer.train()

Evaluate on the held-out validation set using NDCG@10 or MRR as your target metric.
Export and deploy the fine-tuned model by saving it and pointing your reranking pipeline to the new checkpoint.

Verification

python -c "from sentence_transformers import CrossEncoder; \
  m = CrossEncoder('./domain-reranker/checkpoint-200'); \
  print(m.predict(['query text', 'document text']))"

Expected output: A score printed to stdout. Higher scores should correlate with greater relevance. Cross-validate by running the full reranking pipeline against your test set:

python evaluate_reranker.py --model ./domain-reranker/checkpoint-200 \
  --dataset domain_test.jsonl --metric ndcg

Expected output: NDCG@10 score that exceeds the base model's score by a meaningful margin (typically 5–20% in well-prepared domains).

Common failures

Overfitting due to small dataset: Model memorizes training pairs and performs poorly on new queries. Use strong regularization or augmentation.
Data leakage: Validation queries appear in training data, inflating metrics. De-duplicate at the query level before splitting.
GPU out-of-memory: Document texts exceed model max length. Truncate or use sliding windows, but ensure relevant content appears early.
Label noise: Inconsistent human annotations produce a model that learns contradictory signals. Invest in annotation quality over quantity.
Learning rate too high: Causes the model to diverge from pretrained knowledge, degrading performance below the base model.
Version mismatch - The installed package or runtime differs from the command shown; check the version first and rerun the smallest verification command.
Local environment drift - Another service, virtual environment, model, or path is being used; print the active binary path and configuration before changing the guide steps.

Related guides

implement-cascade-reranking-efficiency
improve-embedding-quality-retrieval