AI–Enhanced Benchmarking: New and enhanced features of Open Source and Integration
Author: RahulVadisetty91
Pull request: yes

 #### 1. **Summary**: 
 This PR has brought in several improvements that are based on AI in an attempt to boost the functionality of the benchmarking script. The changes are primarily concerning the ways to refine the artificial intelligence for the purposes of metrics processing, commit analysis, and summary generation. Some of the enhancements include: AI based approach for metric selection in the benchmarking report, automatic commit analysis to determine which changes can be expected to affect the performance, the summary of the key results of the benchmarking, the improved management of the models and configurations. Also, the AI is used for Hugging Face Hub interactions and to validate the data format during the upload process. These changes are useful in enhancing the effectiveness of the benchmarking script, gives out more information and can work with large and complex dataset. 
 
 #### 2. **Related Issues**: 
 - This update addresses the challenge of analyzing the large datasets that was a time consuming and a complex process. 
 - The application of AI in commit analysis helps to solve the problem of figuring out the performance changes of large projects distributed in many commits. 
 - The issue of how to prepare brief and concise reports based on numerous benchmarking outcomes is resolved with the use of Artificial Intelligence for summary generation. 
 - To overcome the compatibility issues in terms of model and configurations, automated artificial intelligence based management was employed. 
 
 #### 3. **Discussions**: 
 The arguments presented were on how the use of artificial intelligence would be of advantage in the benchmarking process. The team also pointed out that the selection of metrics, commits analysis and summarization should also be made automatic in order to enhance the efficiency of the process. Further, there was focus on increasing the robustness of the model and configuration management and to allow for proper interaction with the Hugging Face Hub for data upload. This was to enhance the efficiency of the benchmarking script and to reduce on the possibility of errors. 
 
 #### 4. **QA Instructions**: 
 - **AI-Enhanced Metrics Processing**: Make sure that the AI can choose the right metrics to display in the benchmarking reports and that such metrics are labeled. 
 - **Commit Analysis**: Ensure that the commit analysis based on AI is right in identifying the commits that may have effected changes in the performance either positively or negatively. 
 - **Smart Summary Generation**: It has been observed that the summaries generated from the AI should be brief and should only contain information of comparison of performance and recommendations for improvement. 
 - **Model and Configuration Management**: Make sure that the AI takes into consideration a number of model and configuration parameters and that it gives out an alarm should they interfere in one way or another. 
 - **Hugging Face Hub Integration**: Ensure that the auto upload of results is in order and also ensure that the format of the data is correct if not correct it. 
 
 #### 5. **Merge Plan**: 
 After the QA testing part is over and the developer is satisfied with the performance of the AI components, this branch will be merged with the master branch. Extra focus will be placed on the fact that new features incorporated through the use of AI will not harm the benchmarking activity, but rather improve it. It will be ensured that all the new features which will be included will be fine tuned for the production. 
 
 #### 6. **Motivation and Context**: 
 This is because the benchmark script is to be optimized using artificial intelligence approaches and thus there is need to modify it. Some of the areas that can be automated include the selection of metric, commit analysis, and summary generation; this will ensure that the developers are able to spend their time more constructively and also gain more insight on performance. The inclusion of the AI-based model and configuration management also improve compatibility while the integration with the Hugging Face Hub aids in sharing of data. These changes make the benchmarking process much more accurate, accurate and time efficient. 
 
 #### 7. **Types of Changes**: 
 - **New Feature**: Automation of the commit analysis process, identification of metrics and summarization has been enhanced. 
 - **Enhancement**: Improvement in the model and configuration management for the compatibility and increasing the performance of the system. 
 - **Automation**: Quick and seamless file submission together with the possibility of integrating the model to the Hugging Face Hub and automatic formatting check. 
 - **Performance**: A refined benchmarking process that would give a better analysis of data and reducing the aspect of manual analysis.



## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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