coreb 0.1.0

CoREB: Code Retrieval and Reranking Benchmark — a graded-relevance benchmark for code retrieval and reranking models

CoREB: Code Retrieval and Reranking Benchmark

CoREB is a graded-relevance benchmark for evaluating code retrieval and reranking models across three tasks:

Task	Query	Target	Example
Text-to-Code (T2C)	Natural language description	Code solution	"Find the longest substring without repeating characters" → Python solution
Code-to-Code (C2C)	Code in language A	Equivalent code in language B	Python solution → Java translation
Code-to-Text (C2T)	Code snippet	Problem description	Python solution → problem statement

Key Features

• Graded relevance: 3-level qrel scheme (rel=2: positive, rel=1: hard negative, rel=0: irrelevant) — hard negatives are same-problem distractors that penalize nDCG when retrieved above true positives
• 5 programming languages: Python, C++, Java, Go, Ruby
• Problem-disjoint train/test splits: v202602 (training) and v202603 (testing) cover non-overlapping contest windows
• Drop-in evaluation: compatible with standard IR evaluation (pytrec_eval) with relevance_level=2

Installation

pip install coreb

For HuggingFace model support:

pip install coreb[hf]        # transformers backend
pip install coreb[gemini]    # Google Gemini API
pip install coreb[all]       # everything

Quick Start

Load the Dataset

from datasets import load_dataset

# Load v202603 release (latest)
code_corpus = load_dataset("hq-bench/coreb", "code_corpus", split="release_v2603")
text_corpus = load_dataset("hq-bench/coreb", "text_corpus", split="release_v2603")

# Load task-specific queries and qrels
t2c_queries = load_dataset("hq-bench/coreb", "text2code_queries", split="release_v2603")
t2c_qrels = load_dataset("hq-bench/coreb", "text2code_qrels", split="release_v2603")

print(f"Code corpus: {len(code_corpus)} documents")
print(f"T2C queries: {len(t2c_queries)} queries, {len(t2c_qrels)} qrels")

Run Evaluation

from coreb_runner.benchmark import (
    load_jsonl,
    convert_corpus_to_coir_format,
    convert_queries_to_coir_format,
    convert_qrels_to_coir_format,
    EvaluateRetrieval,
    DenseRetrievalExactSearch,
    create_model_wrapper,
)

# Load data (from local JSONL files or convert from HF datasets)
corpus = convert_corpus_to_coir_format(load_jsonl("code_corpus.jsonl"))
queries = convert_queries_to_coir_format(load_jsonl("text2code_queries.jsonl"))
qrels = convert_qrels_to_coir_format(load_jsonl("text2code_qrels.jsonl"))

# Create model wrapper
model = create_model_wrapper("jinaai/jina-embeddings-v3", model_type="huggingface")

# Run retrieval + evaluation
retriever = DenseRetrievalExactSearch(model, batch_size=64)
evaluator = EvaluateRetrieval(retriever, k_values=[1, 3, 5, 10])
results = evaluator.retrieve(corpus, queries)
ndcg, _map, recall, precision = evaluator.evaluate(qrels, results, evaluator.k_values)

print(f"nDCG@10: {ndcg['NDCG@10']:.4f}")
print(f"Recall@10: {recall['Recall@10']:.4f}")

Evaluation with Graded Relevance

CoREB uses relevance_level=2 — only rel>=2 items count as relevant for binary metrics (Recall, MAP, Precision). Hard negatives (rel=1) penalize nDCG by occupying top ranks with zero gain but do not inflate Recall/MRR.

# The EvaluateRetrieval class handles this automatically:
# - rel=1 (hard negatives) are zeroed out for nDCG computation
# - relevance_level=2 is set for pytrec_eval binary metrics
print(f"Relevance threshold: {EvaluateRetrieval.RELEVANCE_LEVEL}")  # 2

Dataset Structure

Available on HuggingFace: hq-bench/coreb

8 configs x 2 splits (release_v2602, release_v2603):

Config	v2603 Rows	Description
code_corpus	1,744	Code solutions (5 languages, 2 generator models)
text_corpus	875	Problem descriptions (175 original + 700 LLM noise)
text2code_queries	1,123	T2C queries (canonical, full, search subtasks)
text2code_qrels	5,950	T2C relevance judgments (2,814 pos + 3,136 hard neg)
code2code_queries	278	C2C queries (cross-language)
code2code_qrels	1,457	C2C relevance judgments (623 pos + 834 hard neg)
code2text_queries	1,200	C2T queries (canonical, full, match subtasks)
code2text_qrels	4,610	C2T relevance judgments (820 pos + 2,650 hard neg)

Benchmark Results (v202603, nDCG@10)

Rank	Model	Avg	T2C	C2C	C2T
1	GemEmb-2	0.639	0.434	0.698	0.784
2	C2LLM-7B	0.623	0.443	0.659	0.766
3	jina-code-1.5b	0.607	0.414	0.671	0.735
4	C2LLM-0.5B	0.604	0.430	0.657	0.725
5	jina-code-0.5b	0.596	0.386	0.677	0.725
6	F2LLM-4B	0.547	0.407	0.500	0.735
7	Qwen3-Emb-4B	0.495	0.390	0.392	0.704
8	F2LLM-1.7B	0.485	0.383	0.383	0.690
9	Qwen3-Emb-0.6B	0.443	0.349	0.384	0.597
10	F2LLM-0.6B	0.439	0.344	0.334	0.641
11	Qwen3-Emb-8B	0.428	0.328	0.320	0.635

Citation

Coming soon.

License

This project is licensed under the Apache License 2.0 — see LICENSE for details.