added acknowledgement in homepage (#77)

* minor improvement for homepage

* minor fix

README.md

@@ -30,6 +30,10 @@ Data-Juicer is a one-stop data processing system to make data higher-quality,
 juicier, and more digestible for LLMs.
 This project is being actively updated and maintained, and we will periodically enhance and add more features and data recipes. We welcome you to join us in promoting LLM data development and research!
 
+If you find Data-Juicer useful for your research or development, please kindly 
+cite our [work](#references).
+
+
 ----
 
 ## News
@@ -62,7 +66,8 @@ Table of Contents
    * [Demos](#demos)
    * [License](#license)
    * [Contributing](#contributing)
-   * [References](#references)
+  * [Acknowledgement](#acknowledgement)
+  * [References](#references)
 
 ## Features
 
@@ -315,10 +320,24 @@ docker exec -it <container_id> bash
 Data-Juicer is released under Apache License 2.0.
 
 ## Contributing
-We greatly welcome contributions of new features, bug fixes, and discussions. Please refer to [How-to Guide for Developers](docs/DeveloperGuide.md).
+We are in a rapidly developing field and greatly welcome contributions of new 
+features, bug fixes and better documentations. Please refer to 
+[How-to Guide for Developers](docs/DeveloperGuide.md).
 
 Welcome to join our [Slack channel](https://join.slack.com/t/data-juicer/shared_invite/zt-23zxltg9d-Z4d3EJuhZbCLGwtnLWWUDg?spm=a2c22.12281976.0.0.7a8253f30mgpjw), or [DingDing group](https://qr.dingtalk.com/action/joingroup?spm=a2c22.12281976.0.0.7a8253f30mgpjw&code=v1,k1,C0DI7CwRFrg7gJP5aMC95FUmsNuwuKJboT62BqP5DAk=&_dt_no_comment=1&origin=11) for discussion.
 
+## Acknowledgement
+Data-Juicer is used across various LLM products and research initiatives,
+including industrial LLMs from Alibaba Cloud's Tongyi, such as Dianjin for 
+financial analysis, and Zhiwen for reading assistant, as well as the Alibaba 
+Cloud's platform for AI (PAI).
+We look forward to more of your experience, suggestions and discussions for collaboration!
+
+Data-Juicer thanks and refers to several community projects, such as 
+[Huggingface-Datasets](https://github.com/huggingface/datasets), [Bloom](https://huggingface.co/bigscience/bloom), [RedPajama](https://github.com/togethercomputer/RedPajama-Data), [Pile](https://huggingface.co/datasets/EleutherAI/pile), [Alpaca-Cot](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT), [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [DeepSpeed](https://www.deepspeed.ai/), [Arrow](https://github.com/apache/arrow), [Ray](https://github.com/ray-project/ray), [Beam](https://github.com/apache/beam),  [LM-Harness](https://github.com/EleutherAI/lm-evaluation-harness), [HELM](https://github.com/stanford-crfm/helm), ....
+
+
+
 ## References
 If you find our work useful for your research or development, please kindly cite the following [paper](https://arxiv.org/abs/2309.02033).
 ```

README_ZH.md

@@ -29,6 +29,9 @@
 Data-Juicer 是一个一站式数据处理系统，旨在为大语言模型 (LLM) 提供更高质量、更丰富、更易“消化”的数据。
 本项目在积极更新和维护中，我们将定期强化和新增更多的功能和数据菜谱。欢迎您加入我们推进 LLM 数据的开发和研究工作！
 
+如果Data-Juicer对您的研发有帮助，请引用我们的[工作](#参考文献) 。
+
+
 ----
 
 ## 新消息
@@ -61,6 +64,7 @@ Data-Juicer 是一个一站式数据处理系统，旨在为大语言模型 (LLM
   * [演示样例](#演示样例)
   * [开源协议](#开源协议)
   * [贡献](#贡献)
+  * [致谢](#致谢)
   * [参考文献](#参考文献)
 
 ## 特点
@@ -299,10 +303,21 @@ Data-Juicer 在 Apache License 2.0 协议下发布。
 
 ## 贡献
 
-我们非常欢迎贡献新功能、修复漏洞以及讨论。请参考[开发者指南](docs/DeveloperGuide_ZH.md)。
+大模型是一个高速发展的领域，我们非常欢迎贡献新功能、修复漏洞以及文档改善。请参考[开发者指南](docs/DeveloperGuide_ZH.md)。
 
 欢迎加入我们的[Slack channel](https://join.slack.com/t/data-juicer/shared_invite/zt-23zxltg9d-Z4d3EJuhZbCLGwtnLWWUDg?spm=a2c22.12281976.0.0.7a8275bc8g7ypp), 或[DingDing群](https://qr.dingtalk.com/action/joingroup?spm=a2c22.12281976.0.0.7a8275bc8g7ypp&code=v1,k1,C0DI7CwRFrg7gJP5aMC95FUmsNuwuKJboT62BqP5DAk=&_dt_no_comment=1&origin=11) 。
 
+## 致谢
+
+Data-Juicer 被各种 LLM产品和研究工作使用，包括来自阿里云-通义的行业大模型，例如点金
+（金融分析），智文（阅读助手），还有阿里云人工智能平台 (PAI)。 我们期待更多您的体验反馈、建议和合作共建！
+
+
+Data-Juicer 感谢并参考了社区开源项目：
+[Huggingface-Datasets](https://github.com/huggingface/datasets), [Bloom](https://huggingface.co/bigscience/bloom), [RedPajama](https://github.com/togethercomputer/RedPajama-Data), [Pile](https://huggingface.co/datasets/EleutherAI/pile), [Alpaca-Cot](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT), [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [DeepSpeed](https://www.deepspeed.ai/), [Arrow](https://github.com/apache/arrow), [Ray](https://github.com/ray-project/ray), [Beam](https://github.com/apache/beam),  [LM-Harness](https://github.com/EleutherAI/lm-evaluation-harness), [HELM](https://github.com/stanford-crfm/helm), ....
+
+
+
 ## 参考文献
 如果您发现我们的工作对您的研发有帮助，请引用以下[论文](https://arxiv.org/abs/2309.02033) 。
 

thirdparty/README.md

@@ -5,7 +5,7 @@ Dependencies of Auto Evaluation Toolkit, see [`tools/evaluator/README.md`](../to
 ## Installation
 
 The auto-evaluation toolkit requires customized [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and [HELM](https://github.com/stanford-crfm/helm).
-To avoid dependency problems when installing those packages, we recommand using NGC's PyTorch container (`nvcr.io/nvidia/pytorch:22.12-py3`).
+To avoid dependency problems when installing those packages, we recommend using NGC's PyTorch container (`nvcr.io/nvidia/pytorch:22.12-py3`).
 Assuming the path to your shared file system (where your data and model checkpoints are saved) is `/mnt/shared`, start the docker container with following commands.
 
 ```shell