diff --git a/README.md b/README.md
index 5e936c3..cea2461 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ ChatGLM2-6B 开源模型旨在与开源社区一起推动大模型技术发展
 尽管模型在训练的各个阶段都尽力确保数据的合规性和准确性，但由于 ChatGLM2-6B 模型规模较小，且模型受概率随机性因素影响，无法保证输出内容的准确性，且模型易被误导。**本项目不承担开源模型和代码导致的数据安全、舆情风险或发生任何模型被误导、滥用、传播、不当利用而产生的风险和责任。**
 
 ## 评测结果
-我们选取了部分中英文典型数据集进行了评测，以下为 ChatGLM2-6B 模型在 [MMLU](https://github.com/hendrycks/test) (英文)、[C-Eval](https://cevalbenchmark.com/static/leaderboard.html)（中文）、[GSM8K](https://github.com/openai/grade-school-math)（数学）、[BBH](https://github.com/suzgunmirac/BIG-Bench-Hard)（英文） 上的测评结果。
+我们选取了部分中英文典型数据集进行了评测，以下为 ChatGLM2-6B 模型在 [MMLU](https://github.com/hendrycks/test) (英文)、[C-Eval](https://cevalbenchmark.com/static/leaderboard.html)（中文）、[GSM8K](https://github.com/openai/grade-school-math)（数学）、[BBH](https://github.com/suzgunmirac/BIG-Bench-Hard)（英文） 上的测评结果。在 [evaluation](./evaluation/README.md) 中提供了在 C-Eval 上进行测评的脚本。
 
 ### MMLU
 
diff --git a/evaluation/README.md b/evaluation/README.md
new file mode 100644
index 0000000..b888e08
--- /dev/null
+++ b/evaluation/README.md
@@ -0,0 +1,10 @@
+首先从 [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/e84444333b6d434ea7b0) 下载处理好的 C-Eval 数据集，解压到 `evaluation` 目录下。然后运行
+
+```shell
+cd evaluation
+python evaluate_ceval.py
+```
+
+这个脚本会在C-Eval的验证集上进行预测并输出准确率。如果想要得到测试集上的结果可以将代码中的 `./CEval/val/**/*.jsonl` 改为 `./CEval/test/**/*.jsonl`，并按照 C-Eval 规定的格式保存结果并在 [官网](https://cevalbenchmark.com/) 上提交。
+
+汇报的结果使用的是内部的并行测试框架，结果可能会有轻微波动。
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diff --git a/evaluation/evaluate_ceval.py b/evaluation/evaluate_ceval.py
new file mode 100644
index 0000000..bfd317c
--- /dev/null
+++ b/evaluation/evaluate_ceval.py
@@ -0,0 +1,60 @@
+import os
+import glob
+import re
+import json
+import torch
+import torch.utils.data
+from transformers import AutoTokenizer, AutoModel
+from tqdm import tqdm
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
+model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).bfloat16().cuda()
+
+choices = ["A", "B", "C", "D"]
+choice_tokens = [tokenizer.encode(choice, add_special_tokens=False)[0] for choice in choices]
+
+
+def build_prompt(text):
+    return "[Round {}]\n\n问：{}\n\n答：".format(1, text)
+
+
+extraction_prompt = '综上所述，ABCD中正确的选项是：'
+
+accuracy_dict, count_dict = {}, {}
+with torch.no_grad():
+    for entry in glob.glob("./CEval/val/**/*.jsonl", recursive=True):
+        dataset = []
+        with open(entry, encoding='utf-8') as file:
+            for line in file:
+                dataset.append(json.loads(line))
+        correct = 0
+        dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
+        for batch in tqdm(dataloader):
+            texts = batch["inputs_pretokenized"]
+            queries = [build_prompt(query) for query in texts]
+            inputs = tokenizer(queries, padding=True, return_tensors="pt", truncation=True, max_length=2048).to('cuda')
+            outputs = model.generate(**inputs, do_sample=False, max_new_tokens=512)
+            intermediate_outputs = []
+            for idx in range(len(outputs)):
+                output = outputs.tolist()[idx][len(inputs["input_ids"][idx]):]
+                response = tokenizer.decode(output)
+                intermediate_outputs.append(response)
+            answer_texts = [text + intermediate + "\n" + extraction_prompt for text, intermediate in
+                            zip(texts, intermediate_outputs)]
+            input_tokens = [build_prompt(answer_text) for answer_text in answer_texts]
+            inputs = tokenizer(input_tokens, padding=True, return_tensors="pt", truncation=True, max_length=2048).to('cuda')
+            outputs = model(**inputs, return_last_logit=True)
+            logits = outputs.logits[:, -1]
+            logits = logits[:, choice_tokens]
+            preds = logits.argmax(dim=-1)
+            correct += (preds.cpu() == batch["label"]).sum().item()
+        accuracy = correct / len(dataset)
+        print(entry, accuracy)
+        accuracy_dict[entry] = accuracy
+        count_dict[entry] = len(dataset)
+
+acc_total, count_total = 0.0, 0
+for key in accuracy_dict:
+    acc_total += accuracy_dict[key] * count_dict[key]
+    count_total += count_dict[key]
+print(acc_total / count_total)
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