初始化项目，由ModelHub XC社区提供模型
Model: twnlp/ChineseErrorCorrector4-4B Source: Original Platform
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README.md
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README.md
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---
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language:
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- zh
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license: apache-2.0
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tags:
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- chinese
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- text-correction
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- grammatical-error-correction
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- spelling-check
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- qwen3
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- chain-of-thought
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- reinforcement-learning
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base_model: Qwen/Qwen3-4B
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datasets:
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- twnlp/ChineseErrorCorrector
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metrics:
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- f1
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- precision
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- recall
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pipeline_tag: text-generation
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model-index:
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- name: ChineseErrorCorrector4-4B
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results:
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- task:
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type: text-generation
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name: Chinese Grammatical Error Correction
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dataset:
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name: NACGEC
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type: nacgec
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metrics:
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- type: f0.5
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value: 50.99
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- task:
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type: text-generation
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name: Chinese Spelling Check
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dataset:
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name: CSCD
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type: cscd
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metrics:
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- type: f1
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value: 59.61
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---
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# ChineseErrorCorrector4-4B (CSRP)
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<p align="center">
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<a href="https://github.com/TW-NLP/ChineseErrorCorrector">
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<img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github" alt="GitHub"/>
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</a>
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<a href="https://huggingface.co/twnlp/ChineseErrorCorrector4-4B">
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<img src="https://img.shields.io/badge/🤗-Model%20Card-yellow" alt="Hugging Face"/>
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</a>
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<img src="https://img.shields.io/badge/ACL%202026-Oral-red" alt="ACL 2026 Oral"/>
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<img src="https://img.shields.io/badge/License-Apache%202.0-blue" alt="License"/>
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</p>
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---
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## 🔥 Recent Updates
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| Date | Update |
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|------|--------|
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| **2026-05** | 🎉 Paper **"CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards"** accepted as **Oral** at **ACL 2026** |
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| **2026-05** | 🚀 Released **ChineseErrorCorrector4-4B**, achieving new **SOTA** on both NACGEC and CSCD benchmarks |
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---
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## 💡 Introduction
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**ChineseErrorCorrector4-4B** is a high-precision Chinese Grammatical Error Correction (CGEC) and Chinese Spelling Check (CSC) model, built on the **CSRP (CPT → SFT → RL)** three-stage training framework.
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### The Problem: Over-Correction Bias
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Traditional LLM-based correction systems often suffer from **over-correction bias** — models unnecessarily paraphrase correct text rather than leaving it untouched. CSRP resolves this by calibrating decision boundaries through a structured curriculum:
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| Stage | Name | Description |
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|-------|------|-------------|
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| **Phase I** | Balanced Continued Pre-training (CPT) | Internalizes linguistic priors using 5.9M samples with an 8:2 mixture of general and correction-specific data |
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| **Phase II** | Rationale-Augmented SFT | Distills Chain-of-Thought reasoning paths to guide the model in diagnosing error types before executing corrections |
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| **Phase III** | Efficiency-Aware Policy Alignment | Uses **GRPO** with a novel **Efficiency-Aware Reward (EAR)** to penalize unnecessary edits and reward surgical precision |
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---
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## 📊 Benchmark Results
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### 榜单一：中文语法纠错（CGEC）— NACGEC 基准
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针对原生中文及学习者文本，CSRP (4B) 斩获新 SOTA，$F_{0.5}$ 高达 **50.99**，显著超越此前最优专业大模型。
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| 模型 (Scale) | 准确率 Precision | 召回率 Recall | **$F_{0.5}$** (核心指标) |
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|:-------------|:---:|:---:|:---:|
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| BART | 34.67 | 41.88 | 35.91 |
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| HW-CGEC | 50.95 | 32.29 | 45.26 |
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| ScholarGEC (14B) | 45.08 | 59.33 | 47.35 |
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| CEC3 (4B) | 54.20 | 34.75 | 48.74 |
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| **CSRP (4B) [Ours]** ✅ | **57.17** | **35.60** | **50.99** |
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> 🔥 **超越 14B 大模型：** 参数量仅为三成，$F_{0.5}$ 相比 ScholarGEC-14B 提升 **+3.64**！
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>
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> 🔥 **极高准确率 (Precision 57.17%)：** 远超其他模型，最大程度压制了 false-positive（假阳性改写），真正做到"**无错不改，有错必精**"。
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---
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### 榜单二：中文拼写检查（CSC）— CSCD 基准
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CSRP 在字符级纠错 F1 上同样展现出强劲统治力，达到惊人的 **59.61**，全面超越 GPT-4。
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| 模型 | Correction F1 |
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|:-----|:---:|
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| BERT | 25.49 |
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| SoftMask | 44.48 |
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| SMBERT | 44.67 |
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| MDCSpell+ARM | 48.93 |
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| GPT-4 (Few-shot) | 54.41 |
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| **CSRP (4B) [Ours]** ✅ | **59.61** |
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---
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## 🛠️ Quick Start
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### Requirements
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```bash
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pip install -U transformers torch
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```
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> **Note:** Requires `transformers >= 4.51.0` for Qwen3 architecture support.
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### Inference with Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "twnlp/ChineseErrorCorrector4-4B"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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# Professional instruction template
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instruction = (
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"假如你是一名专业的纠错专家，请分析输入句子的语法错误类型和修改原因，"
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"并只输出纠正后的语句，错误类型如下：错别字、词语搭配错误、词性错误、"
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"语序错误、成分残缺、成分赘余、关联词使用错误、指代不明、语义逻辑不通、无误。"
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)
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text_input = "下个星期，我跟我朋唷打算去法国玩儿。"
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messages = [
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{"role": "system", "content": instruction},
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{"role": "user", "content": text_input}
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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do_sample=False,
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repetition_penalty=1.1
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)
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response = tokenizer.decode(
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generated_ids[0][len(model_inputs.input_ids[0]):],
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skip_special_tokens=True
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)
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print(response)
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```
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---
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## 📝 Output Example
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**Input:**
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```
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下个星期，我跟我朋唷打算去法国玩儿。
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```
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**Model Output:**
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```
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<think>
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错误类型：错别字
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修改原因：原句中的"朋唷"是错误写法，正确应为"朋友"。
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"唷"是语气助词，不能用于此处指代同伴。
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正确句使用"朋友"准确表达了与说话者一同前往的人，避免了因错别字造成的语义误解。
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</think>
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下个星期，我跟我朋友打算去法国玩儿。
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```
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**Supported error types:**
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| 错误类型 | 说明 |
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|---------|------|
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| 错别字 | Typos / wrong characters |
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| 词语搭配错误 | Wrong word collocation |
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| 词性错误 | Wrong part of speech |
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| 语序错误 | Wrong word order |
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| 成分残缺 | Missing sentence components |
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| 成分赘余 | Redundant components |
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| 关联词使用错误 | Wrong conjunction usage |
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| 指代不明 | Ambiguous reference |
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| 语义逻辑不通 | Semantic/logical inconsistency |
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| 无误 | No error |
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---
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---
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## 📜 License
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This project is released under the [Apache 2.0 License](LICENSE).
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