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Tutorial — Cross-Encoder Reranking for Medical RAG

This tutorial walks you through the why and the how of fine-tuning a small MS-MARCO cross-encoder on NFCorpus, and then plugging it into a two-stage retrieval pipeline for a medical RAG system.

Audience: an ML engineer who has built a vector store before and now wants better top-k precision than the bi-encoder alone gives.

1. Why a cross-encoder at all?

A bi-encoder (DPR, Sentence-BERT, Instructor, GTE, BGE, …) encodes the query and each document into independent vectors. Similarity is dot-product / cosine. That's fast and scalable but the model never jointly attends to the query and the document, so fine distinctions are lost.

A cross-encoder runs the (query, document) pair through the transformer together — every query token can attend to every document token. That gives much better ranking quality, but the model has to run once per pair, so we can only afford to run it on a shortlist (the bi-encoder's top-K). Hence the two-stage pattern.

                ┌──────────────────┐
   query ─────► │   bi-encoder     │ ──► top-100 docs (fast, recall-oriented)
                └──────────────────┘
                          │
                          ▼
                ┌──────────────────┐
                │  cross-encoder   │ ──► top-5 (slow, precision-oriented)
                └──────────────────┘
                          │
                          ▼
                ┌──────────────────┐
                │       LLM        │ ──► grounded answer
                └──────────────────┘

We use cross-encoder/ms-marco-MiniLM-L-4-v2 because it has a great quality / latency trade-off — 4 transformer layers, ~80 MB, ~10 ms / pair on a single modern GPU.

2. Why NFCorpus?

NFCorpus (Boteva et al. 2016) is the medical IR task in BEIR. It has:

  • 3,633 documents (titles + abstracts from NIH / PubMed-style sources)
  • ~2,600 training queries with graded relevance judgements
  • 323 test queries

It is small enough to iterate on a laptop GPU, and it is genuinely medical — exactly the distribution shift the original MS-MARCO cross-encoder is weakest at.

Download:

from src.data import download_nfcorpus
download_nfcorpus("data")   # -> data/nfcorpus/

The resulting directory has BEIR's standard layout:

data/nfcorpus/
├── corpus.jsonl
├── queries.jsonl
└── qrels/
    ├── train.tsv
    ├── dev.tsv
    └── test.tsv

3. Step-by-step pipeline

3.1 First-stage embeddings

We use hkunlp/instructor-xl as the bi-encoder, with explicit instructions so it knows the domain:

CORPUS_INSTRUCTION = "Represent the medical document for retrieval:"
QUERY_INSTRUCTION  = "Represent the medical query for retrieving relevant documents:"

src/embed_corpus.py produces corpus_embeddings.pkl and query_embeddings.pkl — both dict[str, np.ndarray] keyed by BEIR document / query IDs.

You can swap in any other bi-encoder (BAAI/bge-large-en-v1.5, intfloat/e5-large, …) — just produce the two pickles in the same format.

3.2 Build the FAISS index

from src.data import build_faiss_index
index, id_map = build_faiss_index(corpus_embeddings)

We use IndexFlatL2 because 3.6 k docs is tiny — exact search costs nothing. For larger corpora use IndexIVFFlat or IndexHNSWFlat.

3.3 Hard-negative mining

For every training query, retrieve the top-300 docs from the bi-encoder. Anything not in the ground-truth qrels for that query becomes a hard negative.

Why 300? It needs to be (a) large enough to find genuinely confusing negatives the cross-encoder will struggle with, and (b) small enough that we don't drown the positives. Empirically 100–500 works; 300 was the sweet spot in this dataset.

from src.data import mine_hard_negatives, build_training_pairs

results = mine_hard_negatives(train_qrels, query_embeddings, index, id_map, top_k=300)
samples = build_training_pairs(train_corpus, train_queries, train_qrels, results)
# samples is a list of sentence_transformers.InputExample with label in {0, 1}

In our run this produced ~835 k labelled pairs.

3.4 Fine-tune in chunks

We do NOT train one giant epoch. We slice the shuffled training pool into chunks of 16384 examples and after each chunk we

  • save the cross-encoder
  • rerank the test split's top-100
  • compute NDCG/MAP/Recall/P@k

That gives us a clean learning curve and lets us pick the best checkpoint instead of the last one. The relevant excerpt from src/train.py:

for i in range(chunks):
    chunk = train_samples[i*16384 : (i+1)*16384]
    loader = DataLoader(chunk, shuffle=True, batch_size=164)
    model.fit(
        train_dataloader = loader,
        optimizer_params = {"lr": 5e-6},
        epochs           = 1,
        warmup_steps     = 5000,
        output_path      = "models/ms-marco-MiniLM-L-4-v2-nfcorpus",
        use_amp          = True,
    )
    model.save("models/ms-marco-MiniLM-L-4-v2-nfcorpus")
    print(evaluate(test_qrels,
                   rerank(model, test_corpus, test_queries, test_results, top_k=100),
                   k_values=[1, 3, 5, 10, 100]))

Hyper-parameters that worked well for L-4-v2:

parameter value
batch size 164
learning rate 5e-6
warmup steps 5000
mixed precision yes (AMP)
chunk size 16384
optimizer AdamW (st default)
epochs / chunk 1

3.5 Use the model

from sentence_transformers import CrossEncoder

ce = CrossEncoder("models/ms-marco-MiniLM-L-4-v2-nfcorpus")
scores = ce.predict([
    ("diabetes treatment", "Type 1 and 2 diabetes mellitus: a review …"),
    ("diabetes treatment", "Impact of salt intake on hypertension …"),
])

Score is a single logit per pair — higher = more relevant. There is no fixed cutoff; the model is meant for ranking, not classification.

To plug into a RAG pipeline:

from src.rerank import rerank

# `bi_encoder_results[qid]` is a dict {doc_id: bi-encoder score} of the top 100
reranked = rerank(ce, corpus, queries, bi_encoder_results, top_k=100)
top5 = sorted(reranked[qid].items(), key=lambda kv: -kv[1])[:5]

4. What changes vs. the off-the-shelf cross-encoder?

Looking at reports/training_metrics.csv, the most useful gain is at the top of the list (NDCG@1, P@1), which is exactly what matters for an LLM prompt that only sees a handful of documents:

  • NDCG@1: 0.4242 → 0.4427 ( +1.9 absolute / +4.4% relative )
  • P@1: 0.4396 → 0.4582 ( +1.9 absolute / +4.3% relative )

NDCG@10 and NDCG@100 are essentially unchanged — once you go beyond the top few results, the bi-encoder's ordering already dominates. That's the signature of cross-encoder reranking: it pushes the right answer to position 1.

5. Common pitfalls

  • Don't shuffle qrels into the negative pool. build_training_pairs checks doc_id not in qrels[qid] before labelling something as a negative — losing this check silently destroys training (the model gets told the right answer is wrong).
  • Watch the LR. Cross-encoders need a small LR. 5e-6 is the sweet spot for L-2 / L-4. If you crank it to 1e-4 you'll see all metrics collapse within an epoch.
  • AMP can cause NaNs on tiny GPUs. If you see NaN losses, set use_amp=False and cut the batch size in half.
  • results[query_id] must keep more than top_k entries if you want to rerank exactly top_k. We retrieve 300 at training time but only 100 at eval time.

6. Where this came from

The recipe was developed for a hybrid retrieval stage of a medical-RAG system. The version published here keeps the public datasets (NFCorpus), the public base model (cross-encoder/ms-marco-MiniLM-L-4-v2), and the public bi-encoder (hkunlp/instructor-xl) — no proprietary corpora, queries, or evaluation sets are included.