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Model: hq-bench/coreb-code-reranker
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-Reranker-4B
+tags:
+  - code-search
+  - reranker
+  - code-retrieval
+  - peft
+  - lora
+language:
+  - en
+  - code
+datasets:
+  - hq-bench/coreb
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://hq-bench.github.io/coreb-page/)
+[![arXiv](https://img.shields.io/badge/arXiv-2605.04615-b31b1b.svg)](https://arxiv.org/abs/2605.04615)
+[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/hq-bench/coreb)
+[![Code](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/hq-bench/coreb)
+
+# CoREB-Reranker
+
+**CoREB-Reranker** is a code reranker fine-tuned from [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) via LoRA on a mixed reranker corpus. It is the **only reranker we evaluate that achieves consistent gains across all three code search tasks** (text-to-code, code-to-text, and code-to-code).
+
+## Highlights
+
+- Fine-tuned from Qwen3-Reranker-4B using LoRA (rank=16, alpha=16) on **3.1M training samples** from a mixed corpus
+- Evaluated on CoREB v202603 (problem-disjoint from training set, no data leakage)
+- Achieves **positive reranking delta on all three tasks**, unlike all off-the-shelf rerankers tested
+
+## Reranking Results (nDCG@10 Delta %)
+
+Reranking delta on CoREB v202603, using C2LLM-7B as the first-stage retriever:
+
+| Reranker | Text-to-Code | Code-to-Text | Code-to-Code |
+|----------|:---:|:---:|:---:|
+| Jina Reranker v2 | -8.3 | -22.4 | -8.8 |
+| Jina Reranker v3 | -2.2 | -5.0 | -0.1 |
+| Qwen3-Reranker-0.6B | -0.6 | -8.2 | -2.3 |
+| Qwen3-Reranker-4B | -0.1 | -3.2 | +3.3 |
+| **CoREB-Reranker (ours)** | **+1.1** | **+0.8** | **+5.1** |
+
+## Training Details
+
+- **Base model**: [Qwen/Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B)
+- **Method**: LoRA (rank=16, alpha=16, dropout=0.05)
+- **Target modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training data**: A mixed reranker corpus consisting of [CoREB v202602](https://huggingface.co/datasets/hq-bench/coreb), [CodeSearchNet](https://github.com/github/CodeSearchNet) (code-to-code, code-to-text, text-to-code), [APPS](https://github.com/hendrycks/apps), [CosQA](https://github.com/Jun-jie-Huang/CosQA), and [CodeFeedback](https://github.com/OpenCodeInterpreter/OpenCodeInterpreter) (single-turn and multi-turn). Each record is normalized into binary reranking examples (instruction, query, document, yes/no). Positives are duplicated twice; one easy negative and one hard negative are sampled per record.
+- **Evaluation data**: CoREB v202603 (problem-disjoint from CoREB v202602 training split; covers a different contest time window)
+- **Training samples**: ~3.1M binary reranking examples across text-to-code, code-to-text, and code-to-code tasks
+- **Top-k retrieval for reranking**: 128
+
+## Usage
+
+CoREB-Reranker follows the same usage pattern as Qwen3-Reranker. The instruction is **task-specific** — use the appropriate one for your retrieval task:
+
+```python
+from enum import Enum
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+class Task(Enum):
+    TEXT_TO_CODE = "Given a natural language programming task, retrieve code that correctly solves or implements the task."
+    CODE_TO_CODE = "Given a code snippet, retrieve code that is semantically equivalent or solves the same task."
+    CODE_TO_TEXT = "Given a code snippet, retrieve the natural language description or problem statement that best matches the code."
+
+model_id = "hq-bench/coreb-code-reranker"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, trust_remote_code=True)
+model.eval()
+
+PREFIX = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n'
+SUFFIX = "<|im_end|>\n<|im_start|>assistant\n"
+yes_id = tokenizer.convert_tokens_to_ids("yes")
+no_id = tokenizer.convert_tokens_to_ids("no")
+
+def score(query: str, document: str, task: Task) -> float:
+    prompt = f"{PREFIX}<Instruct>: {task.value}\n<Query>: {query}\n<Document>: {document}{SUFFIX}"
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)
+    with torch.no_grad():
+        logits = model(**inputs).logits[0, -1, :]
+    return (logits[yes_id] - logits[no_id]).item()
+
+# Text-to-Code: natural language query -> code
+print(score(
+    query="binary search implementation",
+    document="def binary_search(arr, target):\n    lo, hi = 0, len(arr) - 1\n    ...",
+    task=Task.TEXT_TO_CODE,
+))
+
+# Code-to-Code: code -> semantically equivalent code
+print(score(
+    query="def binary_search(arr, target): ...",
+    document="int binarySearch(int[] arr, int target) { ... }",
+    task=Task.CODE_TO_CODE,
+))
+
+# Code-to-Text: code -> problem description
+print(score(
+    query="def binary_search(arr, target): ...",
+    document="Find the index of a target value in a sorted array using binary search.",
+    task=Task.CODE_TO_TEXT,
+))
+```
+
+For batch reranking with the CoREB evaluation pipeline, see the [CoREB repository](https://github.com/hq-bench/coreb).
+
+## Citation
+
+```bibtex
+@article{xue2026coreb,
+  title={Beyond Retrieval: A Multitask Benchmark and Reranker for Code Search},
+  author={Xue, Siqiao and Liao, Zihan and Qin, Jin and Zhang, Ziyin and Mu, Yixiang and Zhou, Fan and Yu, Hang},
+  journal={arXiv preprint arXiv:2605.04615},
+  year={2026},
+  url={https://arxiv.org/abs/2605.04615}
+}
+```

chat_template.jinja

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+{%- set instruction = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("Given a web search query, retrieve relevant passages that answer the query") -%}
+{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
+{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
+<|im_start|>system
+Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>
+<|im_start|>user
+<Instruct>: {{ instruction }}
+<Query>: {{ query_text }}
+<Document>: {{ document_text }}<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
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+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
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+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

generation_config.json

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+  "do_sample": true,
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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.6.0"
+}

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tokenizer_config.json

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+  "add_prefix_space": false,
+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}