Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker
In this tutorial, we use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to enhance retrieval high quality. We begin by organising the runtime, loading the reranker, and understanding the way it scores query-document pairs. Then, we transfer from easy pairwise scoring to a sensible two-stage retrieve-and-rerank pipeline, the place a quick bi-encoder first retrieves candidates and zerank-2 reranks them for higher precision. We additionally consider the influence utilizing NDCG@10 and take a look at the reranker throughout finance, authorized, and code examples to evaluate its efficiency in real-world search and rating duties.
!pip -q set up -U "sentence-transformers>=3.0" "transformers>=4.51.0" speed up
import os, time, numpy as np, torch
from sentence_transformers import CrossEncoder, SentenceTransformer, util
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if torch.cuda.is_available():
system = "cuda"
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
print(f"GPU: {torch.cuda.get_device_name(0)} | dtype: {dtype}")
else:
system, dtype = "cpu", torch.float32
print("WARNING: no GPU detected. This 4B mannequin will probably be very gradual on CPU.")
RERANKER_ID = "zeroentropy/zerank-2-reranker"
print(f"nLoading {RERANKER_ID} (~8GB on first run)...")
reranker = CrossEncoder(
RERANKER_ID,
model_kwargs={"torch_dtype": dtype},
system=system,
)
print("Reranker loaded.")
def to_prob(logits):
return (torch.as_tensor(logits, dtype=torch.float32) / 5).sigmoid()We start by putting in the required libraries and importing the principle instruments wanted for reranking and retrieval. We test whether or not a GPU is offered and choose the suitable system and tensor precision for environment friendly mannequin execution. We then load the zeroentropy/zerank-2-reranker mannequin and outline a helper perform to transform uncooked logits into probability-style scores.
print("n" + "="*70 + "nPART 1: Pairwise scoringn" + "="*70)
pairs = [
("What is 2+2?", "4"),
("What is 2+2?", "The answer is definitely 1 million"),
("Which planet is the Red Planet?",
"Mars, known for its reddish appearance, is the Red Planet."),
("Which planet is the Red Planet?",
"Venus is Earth's twin because of its similar size."),
]
logits = reranker.predict(pairs, convert_to_tensor=True)
probs = to_prob(logits)
for (q, d), lg, p in zip(pairs, logits.tolist(), probs.tolist()):
print(f"logit={lg:+6.2f} prob={p:5.3f} | {q[:30]:30s} -> {d[:45]}")̧We take a look at the reranker on easy query-document pairs to grasp the way it scores related and irrelevant solutions. We go every pair via reranker.predict() and obtain uncooked logits from the mannequin. We convert these logits into chances and print the outcomes so we will examine how strongly the mannequin prefers right responses.
print("n" + "="*70 + "nPART 2: mannequin.rank for a single queryn" + "="*70)
question = "How do I repair a Python listing index out of vary error?"
candidates = [
"IndexError happens when you access an index beyond the list length; check len() and loop bounds.",
"Use a try/except IndexError block, or validate the index with `if i < len(lst)` before access.",
"To install Python packages, run `pip install <package>` in your terminal.",
"List comprehensions create new lists: `[x*2 for x in nums]`.",
"Off-by-one errors in vary(len(lst)+1) are a widespread explanation for index out of vary.",
]
rating = reranker.rank(question, candidates, convert_to_tensor=True)
for rank, r in enumerate(rating, 1):
cid = r["corpus_id"]
print(f"#{rank} rating={float(r['score']):+6.2f} prob={to_prob(r['score']):.3f} "
f"| {candidates[cid][:60]}")We use mannequin.rank() to rank a number of candidate solutions for a single question. We present a number of potential explanations for a Python listing index error and let the reranker organize them by relevance. We then print every ranked outcome with its uncooked rating and probability-style rating to see which reply the mannequin considers most helpful.
print("n" + "="*70 + "nPART 3: Two-stage retrieve -> rerank pipelinen" + "="*70)
corpus = [
"The mitochondria is the powerhouse of the cell, producing ATP via respiration.",
"Photosynthesis converts light energy into chemical energy in chloroplasts.",
"ATP synthase uses a proton gradient across the inner mitochondrial membrane to make ATP.",
"DNA replication is semi-conservative and occurs during the S phase of the cell cycle.",
"The Krebs cycle (citric acid cycle) takes place in the mitochondrial matrix.",
"Ribosomes translate mRNA into proteins in the cytoplasm.",
"Glycolysis breaks glucose into pyruvate in the cytosol, yielding a net 2 ATP.",
"The Golgi apparatus modifies, sorts, and packages proteins for secretion.",
"Cellular respiration in mitochondria yields far more ATP than glycolysis alone.",
"Plant cell walls are made primarily of cellulose for structural support.",
]
bi = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", system=system)
corpus_emb = bi.encode(corpus, convert_to_tensor=True, normalize_embeddings=True)
def two_stage_search(q, top_k_retrieve=6, top_n_final=3):
q_emb = bi.encode(q, convert_to_tensor=True, normalize_embeddings=True)
hits = util.semantic_search(q_emb, corpus_emb, top_k=top_k_retrieve)[0]
cand_ids = [h["corpus_id"] for h in hits]
cand_docs = [corpus[i] for i in cand_ids]
rr = reranker.rank(q, cand_docs, convert_to_tensor=True)
out = []
for r in rr[:top_n_final]:
global_id = cand_ids[r["corpus_id"]]
out.append((global_id, corpus[global_id], float(to_prob(r["score"]))))
return cand_ids, out
q = "Where within the cell is most ATP really produced?"
retrieved, ultimate = two_stage_search(q)
print(f"Query: {q}n")
print("Stage 1 (bi-encoder) high order:", retrieved)
print("nStage 2 (zerank-2 reranked) high outcomes:")
for gid, doc, p in ultimate:
print(f" [doc {gid}] prob={p:.3f} | {doc}")We construct a two-stage retrieval pipeline that first makes use of a quick bi-encoder to retrieve candidate paperwork from a small corpus. We then go these retrieved candidates to zerank-2 so it could actually rerank them with deeper query-document understanding. We lastly examine the initially retrieved order with the reranked high outcomes to see how reranking improves precision.
print("n" + "="*70 + "nPART 4: NDCG@10 evaluationn" + "="*70)
eval_set = [
{"query": "Where is most ATP produced in the cell?",
"rels": {0: 2, 2: 3, 4: 2, 6: 1, 8: 3}},
{"query": "How do plants capture light energy?",
"rels": {1: 3, 9: 1}},
{"query": "How are proteins made and packaged in a cell?",
"rels": {5: 3, 7: 2}},
]
def dcg(rels):
rels = np.asarray(rels, dtype=float)
return np.sum((2**rels - 1) / np.log2(np.arange(2, rels.measurement + 2)))
def ndcg_at_k(ranked_doc_ids, rel_map, okay=10):
beneficial properties = [rel_map.get(d, 0) for d in ranked_doc_ids[:k]]
superb = sorted(rel_map.values(), reverse=True)[:k]
idcg = dcg(superb)
return dcg(beneficial properties) / idcg if idcg > 0 else 0.0
base_scores, rr_scores = [], []
for ex in eval_set:
q, rel_map = ex["query"], ex["rels"]
q_emb = bi.encode(q, convert_to_tensor=True, normalize_embeddings=True)
hits = util.semantic_search(q_emb, corpus_emb, top_k=len(corpus))[0]
base_order = [h["corpus_id"] for h in hits]
base_scores.append(ndcg_at_k(base_order, rel_map))
rr = reranker.rank(q, [corpus[i] for i in base_order], convert_to_tensor=True)
rr_order = [base_order[r["corpus_id"]] for r in rr]
rr_scores.append(ndcg_at_k(rr_order, rel_map))
print(f"{'Query':45s} {'bi-encoder':>12s} {'+ zerank-2':>12s}")
for ex, b, r in zip(eval_set, base_scores, rr_scores):
print(f"{ex['query'][:43]:45s} {b:12.4f} {r:12.4f}")
print("-"*72)
print(f"{'AVERAGE NDCG@10':45s} {np.imply(base_scores):12.4f} {np.imply(rr_scores):12.4f}")
print(f"nReranking raise: {np.imply(rr_scores)-np.imply(base_scores):+.4f} NDCG@10")We consider the retrieval pipeline utilizing a small labeled benchmark and the NDCG@10 metric. We first measure the rating high quality of the bi-encoder alone after which measure the standard after making use of zerank-2 reranking. We examine the 2 scores and calculate the reranking raise to evaluate the advance achieved by the cross-encoder.
print("n" + "="*70 + "nPART 5: Cross-domain rerankingn" + "="*70)
domain_cases = {
"finance": ("What does a rising debt-to-equity ratio point out?",
["A higher debt-to-equity ratio means a firm is financing growth with more debt, raising financial risk.",
"EBITDA measures operating performance before interest, taxes, depreciation and amortization.",
"The P/E ratio compares share price to earnings per share."]),
"authorized": ("What is the distinction between a misdemeanor and a felony?",
["Felonies are serious crimes punishable by over a year in prison; misdemeanors carry lighter penalties.",
"A tort is a civil wrong causing harm, separate from criminal law.",
"Habeas corpus protects against unlawful detention."]),
"code": ("How do I reverse a string in Python?",
["Use slicing with a step of -1: `reversed_str = s[::-1]`.",
"`str.be a part of()` concatenates an iterable of strings with a separator.",
"`listing.type()` types a listing in place and returns None."]),
}
for area, (q, docs) in domain_cases.gadgets():
greatest = reranker.rank(q, docs, convert_to_tensor=True)[0]
print(f"[{domain:8s}] {q}n -> prob={to_prob(greatest['score']):.3f} | "
f"{docs[best['corpus_id']][:70]}n")
print("="*70 + "nPART 6: Batched throughputn" + "="*70)
big_query = "What organelle generates mobile power?"
big_docs = corpus * 5
t0 = time.time()
_ = reranker.predict([(big_query, d) for d in big_docs],
batch_size=16, convert_to_tensor=True)
dt = time.time() - t0
print(f"Scored {len(big_docs)} pairs in {dt:.2f}s ({len(big_docs)/dt:.1f} pairs/s)")
print("nDone. zerank-2 is non-commercial (CC-BY-NC-4.0); see the mannequin card for licensing.")We take a look at zerank-2 throughout finance, authorized, and code examples to see the way it handles completely different domains. We then run a batched throughput take a look at by scoring a number of query-document pairs collectively. We end by measuring what number of pairs the reranker processes per second, which provides us a sensible sense of its runtime efficiency.
In conclusion, we constructed a full reranking workflow that exhibits how zerank-2 improves the standard of retrieved outcomes past primary embedding similarity. We noticed how uncooked logits may be transformed into probability-style scores, how mannequin.rank helps order candidate passages, and the way a reranker can match naturally into retrieval-augmented technology or semantic search programs. We additionally benchmarked the reranking raise and measured batched throughput, giving us a sensible view of each accuracy and efficiency. Also, we realized find out how to use zerank-2 as a robust precision layer for search, RAG, authorized retrieval, monetary evaluation, and code-focused doc rating.
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