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README.md

@@ -18,61 +18,123 @@ inference: false
 <br>
 
 <p align="center">
-        Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ &nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md">Report</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/9bjvspyu">Discord</a>
-
+        Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ Qwen-VL-Chat-Int4 <a href="https://huggingface.co/Qwen/Qwen-VL-Chat-Int4">🤗</a>
+<br>
+<a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Report</a>
 </p>
 <br>
 
-**Qwen-VL** 是阿里云研发的大规模视觉语言模型（Large Vision Language Model, LVLM）。Qwen-VL 可以以图像、文本、检测框作为输入，并以文本和检测框作为输出。Qwen-VL 系列模型的特点包括：
-- **强大的性能**：在四大类多模态任务的标准英文测评中（Zero-shot Caption/VQA/DocVQA/Grounding）上，均取得同等通用模型大小下最好效果；
-- **多语言对话模型**：天然支持多语言对话，端到端支持图片里中英双语的长文本识别；
-- **多图交错对话**：支持多图输入和比较，指定图片问答，多图文学创作等；
-- **首个支持中文开放域定位的通用模型**：通过中文开放域语言表达进行检测框标注；
-- **细粒度识别和理解**：相比于目前其它开源LVLM使用的224分辨率，Qwen-VL是首个开源的448分辨率的LVLM模型。更高分辨率可以提升细粒度的文字识别、文档问答和检测框标注。
+**Qwen-VL** 是阿里云研发的大规模视觉语言模型（Large Vision Language Model, LVLM）。Qwen-VL 可以以图像、文本、检测框作为输入，并以文本和检测框作为输出。Qwen-VL 系列模型性能强大，具备多语言对话、多图交错对话等能力，并支持中文开放域定位和细粒度图像识别与理解。
 
 **Qwen-VL** (Qwen Large Vision Language Model) is the visual multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. The features of Qwen-VL include:
-- **Strong performance**: It significantly surpasses existing open-source Large Vision Language Models (LVLM) under similar scale settings on multiple English evaluation benchmarks (including Zero-shot caption, VQA, DocVQA, and Grounding).
-- **Multi-lingual LVLM support text recognization**: Qwen-VL naturally supports multi-lingual conversation, and it promotes end-to-end recognition of Chinese and English bi-lingual text in images.
-- **Multi-image interleaved conversations**: This feature allows for the input and comparison of multiple images, as well as the ability to specify questions related to the images and engage in multi-image storytelling.
-- **First generalist model support grounding in Chinese**: Detecting bounding boxes through open-domain language expression in both Chinese and English.
-- **Fine-grained recognization and understanding**: Compared to the 224 resolution currently used by other open-source LVLM, the 448 resolution promotes fine-grained text recognition, document QA, and bounding box annotation.
 
-目前，我们提供了 Qwen-VL 系列的两个模型：
-- Qwen-VL: Qwen-VL 以 Qwen-7B 的预训练模型作为语言模型的初始化，并以 [Openclip ViT-bigG](https://github.com/mlfoundations/open_clip) 作为视觉编码器的初始化，中间加入单层随机初始化的 cross-attention，经过约1.5B的图文数据训练得到。最终图像输入分辨率为448。
-- Qwen-VL-Chat: 在 Qwen-VL 的基础上，我们使用对齐机制打造了基于大语言模型的视觉AI助手Qwen-VL-Chat，其训练数据涵盖了 QWen-7B 的纯文本 SFT 数据、开源 LVLM 的 SFT 数据、数据合成和人工标注的图文对齐数据。
+目前，我们提供了Qwen-VL和Qwen-VL-Chat两个模型，分别为预训练模型和Chat模型。如果想了解更多关于模型的信息，请点击[链接](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md)查看我们的技术备忘录。本仓库为Qwen-VL-Chat仓库。
+
+We release Qwen-VL and Qwen-VL-Chat, which are pretrained model and Chat model respectively. For more details about Qwen-VL, please refer to our [technical memo](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md). This repo is the one for Qwen-VL.
+<br>
+
+## 安装要求 (Requirements)
+
+* python 3.8及以上版本
+* pytorch 1.12及以上版本，推荐2.0及以上版本
+* 建议使用CUDA 11.4及以上（GPU用户需考虑此选项）
+* python 3.8 and above
+* pytorch 1.12 and above, 2.0 and above are recommended
+* CUDA 11.4 and above are recommended (this is for GPU users)
+  <br>
+
+## 快速开始 (Quickstart)
+
+我们提供简单的示例来说明如何利用 🤗 Transformers 快速使用 Qwen-VL。
+
+在开始前，请确保你已经配置好环境并安装好相关的代码包。最重要的是，确保你满足上述要求，然后安装相关的依赖库。
 
-如果想了解更多关于模型的信息，请点击[链接](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md)查看我们的技术备忘录。
+Below, we provide simple examples to show how to use Qwen-VL with 🤗 Transformers.
 
-We release two models of the Qwen-VL series:
-- Qwen-VL: The pre-trained LVLM model uses Qwen-7B as the initialization of the LLM, and [Openclip ViT-bigG](https://github.com/mlfoundations/open_clip) as the initialization of the visual encoder. And connects them with a randomly initialized cross-attention layer. Qwen-VL was trained on about 1.5B image-text paired data.
-- Qwen-VL-Chat: A multimodal LLM-based AI assistant, which is trained with alignment techniques.
+Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
 
-For more details about Qwen-VL, please refer to our [technical memo](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md).
+```bash
+pip install -r requirements.txt
+```
+
+接下来你可以开始使用Transformers来使用我们的模型。关于视觉模块的更多用法，请参考[教程](TUTORIAL.md)。
+
+Now you can start with Transformers. More usage aboue vision encoder, please refer to [tutorial](TUTORIAL_zh.md).
+
+#### 🤗 Transformers
+
+To use Qwen-VL for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, **please make sure that you are using the latest code.**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+torch.manual_seed(1234)
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+
+# use bf16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, bf16=True).eval()
+# use fp16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, fp16=True).eval()
+# use cpu only
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cpu", trust_remote_code=True).eval()
+# use cuda device
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()
+
+# Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0)
+# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+
+query = tokenizer.from_list_format([
+    {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'},
+    {'text': 'Generate the caption in English with grounding:'},
+])
+inputs = tokenizer(query, return_tensors='pt')
+inputs = inputs.to(model.device)
+pred = model.generate(**inputs)
+response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
+print(response)
+# <img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>Generate the caption in English with grounding:<ref> Woman</ref><box>(451,379),(731,806)</box> and<ref> her dog</ref><box>(219,424),(576,896)</box> playing on the beach<|endoftext|>
+image = tokenizer.draw_bbox_on_latest_picture(response)
+if image:
+  image.save('2.jpg')
+else:
+  print("no box")
+```
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo_spotting_caption.jpg" width="500"/>
+<p>
+<br>
 
 ## 评测
 
 我们从两个角度评测了两个模型的能力：
-1. 在**英文标准 Benchmark** 上评测模型的基础任务能力。目前评测了四大类多模态任务：
-    - Zero-shot Caption: 评测模型在未见过数据集上的零样本图片描述能力；
-    - General VQA: 评测模型的通用问答能力，例如判断题、颜色、个数、类目等问答能力；
-    - Text-based VQA：评测模型对于图片中文字相关的识别/问答能力，例如文档问答、图表问答、文字问答等；
-    - Referring Expression Compression：评测模型给定物体描述画检测框的能力；
 
+1. 在**英文标准 Benchmark** 上评测模型的基础任务能力。目前评测了四大类多模态任务：
+   
+   - Zero-shot Caption: 评测模型在未见过数据集上的零样本图片描述能力；
+   - General VQA: 评测模型的通��问答能力，例如判断题、颜色、个数、类目等问答能力；
+   - Text-based VQA：评测模型对于图片中文字相关的识别/问答能力，例如文档问答、图表问答、文字问答等；
+   - Referring Expression Compression：评测模型给定物体描述画检测框的能力；
 2. **试金石 (TouchStone)**：为了评测模型整体的图文对话能力和人类对齐水平。我们为此构建了一个基于 GPT4 打分来评测 LVLM 模型的 Benchmark：TouchStone。在 TouchStone-v0.1 中：
-    - 评测基准总计涵盖 300+张图片、800+道题目、27个类别。包括基础属性问答、人物地标问答、影视作品问答、视觉推理、反事实推理、诗歌创作、故事写作，商品比较、图片解题等**尽可能广泛的类别**。
-    - 为了弥补目前 GPT4 无法直接读取图片的缺陷，我们给所有的带评测图片提供了**人工标注的充分详细描述**，并且将图片的详细描述、问题和模型的输出结果一起交给 GPT4 打分。
-    - 评测同时包含英文版本和中文版本。
-  
+   
+   - 评测基准总计涵盖 300+张图片、800+道题目、27个类别。包括基础属性问答、人物地标问答、影视作品问答、视觉推理、反事实推理、诗歌创作、故事写作，商品比较、图片解题等**尽可能广泛的类别**。
+   - 为了弥补目前 GPT4 无法直接读取图片的缺陷，我们给所有的带评测图片提供了**人工标注的充分详细描述**，并且将图片的详细描述、问题和模型的输出结果一起交给 GPT4 打分。
+   - 评测同时包含英文版本和中文版本。
+
 评测结果如下：
 
 We evaluated the model's ability from two perspectives:
+
 1. **Standard Benchmarks**: We evaluate the model's basic task capabilities on four major categories of multimodal tasks:
+   
    - Zero-shot Caption: Evaluate model's zero-shot image captioning ability on unseen datasets;
    - General VQA: Evaluate the general question-answering ability of pictures, such as the judgment, color, number, category, etc;
    - Text-based VQA: Evaluate the model's ability to recognize text in pictures, such as document QA, chart QA, etc;
    - Referring Expression Comprehension: Evaluate the ability to localize a target object in an image described by a referring expression.
-
 2. **TouchStone**: To evaluate the overall text-image dialogue capability and alignment level with humans, we have constructed a benchmark called TouchStone, which is based on scoring with GPT4 to evaluate the LVLM model.
+   
    - The TouchStone benchmark covers a total of 300+ images, 800+ questions, and 27 categories. Such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc;
    - In order to break the current limitation of GPT4 in terms of direct image input, TouchStone provides fine-grained image annotations by human labeling. These detailed annotations, along with the questions and the model's output, are then presented to GPT4 for scoring.
    - The benchmark includes both English and Chinese versions.
@@ -85,7 +147,8 @@ Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has
     <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/radar.png" width="600"/>
 <p>
 
-### Zero-shot Captioning & General VQA
+### 零样本图像描述 & 通用视觉问答 (Zero-shot Captioning & General VQA)
+
 <table>
 <thead>
   <tr>
@@ -242,11 +305,10 @@ Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has
 
 - 在 Zero-shot Caption 中，Qwen-VL 在 Flickr30K 数据集上取得了 **SOTA** 的结果，并在 Nocaps 数据集上取得了和 InstructBlip 可竞争的结果。
 - 在 General VQA 中，Qwen-VL 取得了 LVLM 模型同等量级和设定下 **SOTA** 的结果。
-
 - For zero-shot image captioning, Qwen-VL achieves the **SOTA** on Flickr30K and competitive results on Nocaps with InstructBlip.
 - For general VQA, Qwen-VL achieves the **SOTA** under the same generalist LVLM scale settings.
 
-### Text-oriented VQA (focuse on text understanding capabilities in images)
+### 文本导向的视觉问答 (Text-oriented VQA)
 
 <table>
 <thead>
@@ -316,11 +378,11 @@ Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has
 
 - 在文字相关的识别/问答评测上，取得了当前规模下通用 LVLM 达到的最好结果。
 - 分辨率对上述某几个评测非常重要，大部分 224 分辨率的开源 LVLM 模型无法完成以上评测，或只能通过切图的方式解决。Qwen-VL 将分辨率提升到 448，可以直接以端到端的方式进行以上评测。Qwen-VL 在很多任务上甚至超过了 1024 分辨率的 Pic2Struct-Large 模型。
-
 - In text-related recognition/QA evaluation, Qwen-VL achieves the SOTA under the generalist LVLM scale settings.
 - Resolution is important for several above evaluations. While most open-source LVLM models with 224 resolution are incapable of these evaluations or can only solve these by cutting images, Qwen-VL scales the resolution to 448 so that it can be evaluated end-to-end. Qwen-VL even outperforms Pic2Struct-Large models of 1024 resolution on some tasks.
 
-### Referring Expression Comprehension
+### 细粒度视觉定位 (Referring Expression Comprehension)
+
 <table>
 <thead>
   <tr>
@@ -490,13 +552,13 @@ Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has
 
 We provide all of the above evaluation scripts for reproducing our experimental results. Please read [eval/EVALUATION.md](eval/EVALUATION.md) for more information.
 
-### Chat evaluation
+### 闲聊能力测评 (Chat Evaluation)
 
 TouchStone 是一个基于 GPT4 打分来评测 LVLM 模型的图文对话能力和人类对齐水平的基准。它涵盖了 300+张图片、800+道题目、27个类别，包括基础属性、人物地标、视觉推理、诗歌创作、故事写作、商品比较、图片解题等**尽可能广泛的类别**。关于 TouchStone 的详细介绍，请参考[touchstone/README_CN.md](touchstone/README_CN.md)了解更多信息。
 
 TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities of the LVLM model on text-image dialogue and alignment levels with humans. It covers a total of 300+ images, 800+ questions, and 27 categories, such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc. Please read [touchstone/README_CN.md](touchstone/README.md) for more information.
 
-#### English evaluation
+#### 英语 (English)
 
 | Model         | Score |
 |---------------|-------|
@@ -508,7 +570,7 @@ TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities o
 | LLaVA         | 602.7 |
 | Qwen-VL-Chat   | 645.2 |
 
-#### Chinese evaluation
+#### 中文 (Chinese)
 
 | Model         | Score |
 |---------------|-------|
@@ -518,95 +580,39 @@ TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities o
 Qwen-VL-Chat 模型在中英文的对齐评测中均取得当前 LVLM 模型下的最好结果。
 
 Qwen-VL-Chat has achieved the best results in both Chinese and English alignment evaluation.
+<br>
 
-## Requirements
-
-* python 3.8及以上版本
-* pytorch 1.12及以上版本，推荐2.0及以上版本
-* 建议使用CUDA 11.4及以上（GPU用户需考虑此选项）
-
-* python 3.8 and above
-* pytorch 1.12 and above, 2.0 and above are recommended
-* CUDA 11.4 and above are recommended (this is for GPU users)
-
-## Quickstart
-
-我们提供简单的示例来说明如何利用 🤗 Transformers 快速使用 Qwen-VL 和 Qwen-VL-Chat。
-
-在开始前，请确保你已经配置好环境并安装好相关的代码包。最重要的是，确保你满足上述要求，然后安装相关的依赖库。
-
-Below, we provide simple examples to show how to use Qwen-VL and Qwen-VL-Chat with 🤗 Transformers.
-
-Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
-
-```bash
-pip install -r requirements.txt
-```
+## 常见问题 (FAQ)
 
-接下来你可以开始使用Transformers来使用我们的模型。关于视觉模块的更多用法，请参考[教程](TUTORIAL.md)。
+如遇到问题，敬请查阅 [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。
 
-Now you can start with Transformers. More usage aboue vision encoder, please refer to [tutorial](TUTORIAL_zh.md).
+If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ.md) and the issues first to search a solution before you launch a new issue.
+<br>
 
-#### 🤗 Transformers
+## 使用协议 (License Agreement)
 
-To use Qwen-VL for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, **please make sure that you are using the latest code.**
+研究人员与开发者可使用Qwen-VL和Qwen-VL-Chat或进行二次开发。我们同样允许商业使用，具体细节请查看[LICENSE](https://github.com/QwenLM/Qwen-VL/blob/master/LICENSE)。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/qianwen)申请。
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from transformers.generation import GenerationConfig
-import torch
-torch.manual_seed(1234)
+Researchers and developers are free to use the codes and model weights of both Qwen-VL and Qwen-VL-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details.
+<br>
 
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+## 引用 (Citation)[](https://)
 
-# use bf16
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, bf16=True).eval()
-# use fp16
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, fp16=True).eval()
-# use cpu only
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cpu", trust_remote_code=True).eval()
-# use cuda device
-model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()
+如果你觉得我们的论文和代码对你的研究有帮助，请考虑:star: 和引用 :pencil: :)
 
-# Specify hyperparameters for generation
-model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)
 
-query = tokenizer.from_list_format([
-    {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'},
-    {'text': 'Generate the caption in English with grounding:'},
-])
-inputs = tokenizer(query, return_tensors='pt')
-inputs = inputs.to(model.device)
-pred = model.generate(**inputs)
-response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
-print(response)
-# <img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>Generate the caption in English with grounding:<ref> Woman</ref><box>(451,379),(731,806)</box> and<ref> her dog</ref><box>(219,424),(576,896)</box> playing on the beach<|endoftext|>
-image = tokenizer.draw_bbox_on_latest_picture(response)
-if image:
-  image.save('2.jpg')
-else:
-  print("no box")
+```BibTeX
+@article{Qwen-VL,
+  title={Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities},
+  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2308.12966},
+  year={2023}
+}
 ```
+<br>
 
-<p align="center">
-    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo_spotting_caption.jpg" width="500"/>
-<p>
-
-
-## FAQ
-
-如遇到问题，敬请查阅 [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。
-
-If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ.md) and the issues first to search a solution before you launch a new issue.
-
-
-## License Agreement
-
-研究人员与开发者可使用Qwen-VL和Qwen-VL-Chat或进行二次开发。我们同样允许商业使用，具体细节请查看[LICENSE](https://github.com/QwenLM/Qwen-VL/blob/master/LICENSE)。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/qianwen)申请。
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-Researchers and developers are free to use the codes and model weights of both Qwen-VL and Qwen-VL-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details.
-
-## Contact Us
+## 联系我们 (Contact Us)
 
 如果你想给我们的研发团队和产品团队留言，请通过邮件（qianwen_opensource@alibabacloud.com）联系我们。