Update README.md

README.md

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 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 ---
+# CFunModel: A Comprehensive Language Model for Chinese Humor Understanding and Generation
 
-Model for our paper CFunModel:  A "Funny" Language Model Capable of Chinese Humor Generation and Processing
+CFunModel is a comprehensive language model designed for Chinese humor understanding, generation, and processing. Built on top of **Qwen2.5-7B-Instruct**, CFunModel is fine-tuned on **CFunSet**, a diverse multi-task dataset that aggregates various Chinese humor-related tasks. 
+
+CFunModel outperforms several existing large language models in humor-related tasks, including joke generation, humor recognition, crosstalk response selection, and humor explanation, etc.
+
+
+### 🔥 Key Features
+- 🎭 **Multi-Task Capability:** Supports joke generation, humor recognition, crosstalk continuation, humor explanation, and more.
+- 📚 **Extensive Fine-Tuning:** Trained on over **160,000+** humor-related samples aggregated from Tieba-JokeBar, CrossDial, Chumor, HumorWB, and other datasets.
+- 🎯 **High Performance:** Consistently achieves superior results in humor tasks compared to baseline models like GPT-4o, DeepSeek-V3, and Qwen2.5-7B-Instruct.
+- 🧠 **Understanding and Generation:** Excels at generating humorous responses, explaining humor, and classifying humorous content.
+
+## 🎯 Supported Tasks
+
+CFunModel is capable of solving a variety of humor-related tasks, here are several examples:
+
+### 1. **Joke Continuation**
+- **Prompt:** 我将给你笑话的第一句话，请你生成整个笑话。笑话的第一句话如下：
+- **Input:** 我对女儿说：再看10分钟的动画片就该睡觉了。
+- **Output:** 她抗议说时间太短。“那就600秒，够长了吧？”我说。女儿说：“够了，够了。”
+
+### 2. **Crosstalk Response Selection**
+- **Prompt:** 给定一段相声台词，请从多个备选项中选择最合适的逗哏回复。
+- **Input:** 已有台词：我是肖飞。|噢。|给大家说一段。|好。|吃五谷杂粮没有不得病的。|是。|我是大病上医院，小病上药店。|小病干嘛不找大夫呀？|挂号费够买药的了。|噢。
+候选回复：早上天还没亮，三狗屁就来到了工程队。
+反正很重，去买药，下台阶儿。
+那天我脚气犯了，上药店买点儿药吧。
+都快半夜了，上哪找药店去？
+- **Output:** 那天我脚气犯了，上药店买点儿药吧。
+
+### 3. **Humor Recognition**
+- **Prompt:** 以下是一段文本，请分析它是否具有幽默性。幽默性指该文本是否可能引起读者发笑，或通过语言技巧（如双关语、讽刺、夸张、荒诞或逻辑上的意外）营造幽默效果。只需要输出“幽默”或“不幽默”。
+- **Input:** 问一下已经步入社会的大人们，假如有机会带着现在的记忆回到高一那年，你最想做什么？
+- **Output:** 不幽默
+
+### 4. **Joke Continuation**
+- **Prompt:** 生成一个关键词为“砍价”的笑话。
+- **Input:** -
+- **Output:** 老板：一块五一斤。|我：太贵了，五块钱三斤吧。|老板：不行不行。
+
+### 5. **Crosstalk Continuation**
+- **Prompt:** 以下是一段相声的前一半内容，请你根据给定的部分续写完成这段相声。
+- **Input:** 0:“常言道，烟酒不分家。来，抽一根儿!”|1:“你小子真鬼，用你的孬烟换我的好酒!”|0:“吝啬鬼，你能喝什么好酒?”|1:“谁不知道你呀，犯了瘾十烟屁股吸。”|0:“你馋的不要脸，蹭人家的酒喝。”
+- **Output:** 1:“你讨厌讨厌真讨厌，公共场合放毒烟;害人害己害环境，吞云吐雾造污染。浪费开支烧金钱，口干舌燥得咽炎;慢性自杀损寿命，戒烟戒烟快戒烟!”|0:“你混蛋混蛋真混蛋，一喝就高吐一滩;伤头伤胃伤心肝，醉生梦死早完蛋……”|1:“你完蛋!”|0:“你完蛋!”|1:“你是烟鬼，你滚蛋!”
+
+---
+
+### 📊 Model Performance
+
+CFunModel consistently achieves high performance across multiple humor-related tasks. Below is a performance comparison of CFunModel with other state-of-the-art models:
+
+| **Model**              | **Dougen Response (Acc)** | **Penggen Response (Acc)** | **HumorWB (Acc)** |
+|------------------------|----------------------------|-----------------------------|-------------------|
+| GPT-4o                 | 79.67                      | 73.88                       | 83.41             |
+| GPT-4o mini            | 74.14                      | 67.45                       | 84.78             |
+| DeepSeek-V3            | 83.66                      | 78.16                       | 85.15             |
+| Qwen2.5-7B-Instruct    | 24.74                      | 20.87                       | 79.56             |
+| ERNIE                  | 84.54                      | -                           | -                 |
+| RoBERTa                | -                          | 76.19                       | -                 |
+| **CFunModel (Ours)**    | **91.70**                  | **88.99**                   | **85.98**         |
+
+✅ CFunModel significantly improves on the base model, especially in humor-related tasks, showcasing superior performance and understanding.
+
+---
+### Quickstart
+
+Here provides a code similar with the structure of Qwen2.5-7B-Instruct to show you how to use CFunModel to generate humor-related answers. 
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "Qwen/Qwen2.5-7B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "生成一个主题为家庭琐事的笑话。"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```