---
title: ERank-14B
canonical_url: "https://www.modelscope.cn/models/iic/ERank-14B"
md_url: "https://www.modelscope.cn/models/iic/ERank-14B.md"
repository: iic/ERank-14B
chinese_name: ERank-14B
last_updated: 2025-09-28
license: apache-2.0
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- qwen3
architectures:
- Qwen3ForCausalLM
parameters: 14.8B
tensor_type:
- BF16
library_name:
- safetensors
- pytorch
frameworks:
- Pytorch
inference_backends:
- "deploy_task text/emb"
- "lmdeploy_turbomind 0.9.1"
- "sglang 0.5.2"
- "vllm 0.9.2"
downloads: 212
stars: 0
---
# ERank-14B
> ERank-14B - iic 在 ModelScope 开源的模型。ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
iic/ERank-14B 是 ModelScope 魔搭社区上的 14.8B 参数text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。
- **Repository**: iic/ERank-14B
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 14.8B
- **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 212
- **Stars**: 0
- **Last updated**: 2025-09-28
Source: https://www.modelscope.cn/models/iic/ERank-14B
---
ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Arxiv
## Introduction
We introduce ERANK, a highly effective and efficient pointwise reranker built from a reasoning LLM, which excels across diverse relevance scenarios with low latency.
Surprisingly, it also outperforms recent listwise rerankers on the most challenging reasoning-intensive tasks.
ERank is trained with a novel two-stage training pipeline, i.e., Supervised Fine-Tuning (SFT) and Reinforcement
Learning (RL).
During the SFT stage, unlike traidtional pointwise rerankers that train the LLMs for binary relevance classification, we encourage the LLM to generatively output fine grained integer scores.
In the RL training, we introduce a novel listwise derived reward, which instills global ranking awareness into the efficient
pointwise architecture.
## Model List
We provide the trained reranking models in various sizes (4B, 14B and 32B), all of which support customizing the input instruction according to different tasks.
| Model | Size | Layers | Sequence Length | Instruction Aware |
|------------------------------------------|------|--------|-----------------|-------------------|
| [ERank-4B](https://huggingface.co/Alibaba-NLP/ERank-4B) | 4B | 36 | 32K | Yes |
| [ERank-14B](https://huggingface.co/Alibaba-NLP/ERank-14B) | 14B | 40 | 128K | Yes |
| [ERank-32B](https://huggingface.co/Alibaba-NLP/ERank-32B) | 32B | 64 | 128K | Yes |
## Evaluation
We evaluate ERank on both reasoning-intensive benchmarks (BRIGHT and FollowIR) and traditional semantic relevance benchmarks (BEIR and TREC DL).
All methods use the original queries without hybrid scores.
| Paradigm | Method | Average | BRIGHT | FollowIR | BEIR | TREC DL |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| - | First-stage retriever | 25.9 | 13.7 | 0 | 40.8 | 49.3 |
| Listwise | Rank-R1-7B | 34.6 | 15.7 | 3.6 | **49.0** | 70.0 |
| Listwise | Rearank-7B | 35.3 | 17.4 | 2.3 | **49.0** | **72.5** |
| Pointwise | JudgeRank-8B | 32.1 | 17.0 | 9.9 | 39.1 | 62.6 |
| Pointwise | Rank1-7B | 34.6 | 18.2 | 9.1 | 44.2 | 67.1 |
| Pointwise | **ERank-4B (Ours)** | 36.8 | 22.7 | 11.0 | 44.8 | 68.9 |
| Pointwise | **ERank-14B (Ours)** | 36.9 | 23.1 | 10.3 | 47.1 | 67.1 |
| Pointwise | **ERank-32B (Ours)** | **38.1** | **24.4** | **12.1** | 47.7 | 68.1 |
On the most challenging BRIGHT benchmark, with top-100 documents retrieved by ReasonIR-8B using GPT-4 reason-query, ERank with BM25 hybrid achieves the state-of-the-art NDCG@10.
| Method | nDCG@10 |
| :--- | :--- |
| ReasonIR-8B | 30.5 |
| Rank-R1-7B | 24.1 |
| Rank1-7B | 24.3 |
| Rearank-7B | 27.5 |
| JudgeRank-8B | 20.2 |
| *+ BM25 hybrid* | 22.7 |
| Rank-R1-32B-v0.2 | 37.7 |
| *+ BM25 hybrid* | 40.0 |
| **ERank-4B (Ours)** | 30.5 |
| *+ BM25 hybrid* | 38.7 |
| **ERank-14B (Ours)** | 31.8 |
| *+ BM25 hybrid* | 39.3 |
| **ERank-32B (Ours)** | 32.8 |
| *+ BM25 hybrid* | **40.2** |
Since ERank is a pointwise reranker, it has low latency compared with listwise models.
For more details, please refer to our [Paper](https://arxiv.org/abs/2509.00520).
## Usage
We have implemented the inference code based on Transformer and vLLM, respectively.
```python
from examples.ERank_Transformer import ERank_Transformer
from examples.ERank_vLLM import ERank_vLLM
from examples.utils import hybrid_scores
# select a model
# model_name_or_path = "Alibaba-NLP/ERank-4B"
model_name_or_path = "Alibaba-NLP/ERank-14B"
# model_name_or_path = "Alibaba-NLP/ERank-32B"
# use vLLM or Transformer
# reranker = ERank_Transformer(model_name_or_path)
reranker = ERank_vLLM(model_name_or_path)
# input data
instruction = "Retrieve relevant documents for the query."
query = "I am happy"
docs = [
{"content": "excited", "first_stage_score": 46.7},
{"content": "sad", "first_stage_score": 1.5},
{"content": "peaceful", "first_stage_score": 2.3},
]
# rerank
results = reranker.rerank(query, docs, instruction, truncate_length=2048)
print(results)
# [
# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84},
# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98}
# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0},
# ]
# Optional: hybrid with first-stage scores
alpha = 0.2
hybrid_results = hybrid_scores(results, alpha)
print(hybrid_results)
# [
# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84, 'hybrid_score': 1.18},
# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98, 'hybrid_score':0.01},
# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0, 'hybrid_score': -1.19}
# ]
```
Please refer to the `examples` directory for details, in which we also provide the instructions used in the prompt during evaluation.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{ERank,
title={ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking},
author={Yuzheng Cai and Yanzhao Zhang and Dingkun Long and Mingxin Li and Pengjun Xie and Weiguo Zheng},
year={2025},
eprint={2509.00520},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2509.00520},
}
```
