Report On: Fetch issues

Analysis of Open Issues for the Software Project

Notable Open Issues

Translation Efforts

• Issue #70: Feat: Spanish Version - This issue is critical for expanding the project's reach to Spanish-speaking users. The translation of all pages remains a TODO, which is significant given the potential user base impact.
• Issue #67: Update left articles Chinese version - Similar to #70, this issue focuses on updating and translating articles into Chinese. It's important to ensure translations are accurate and up-to-date to serve the Chinese-speaking community effectively.
• Issue #34: Translate to Simplified Chinese (zh-CN) - This issue indicates ongoing efforts to translate the cookbook into Simplified Chinese. It's notable that there was a bug related to language prefix in file names (#40), which seems to have been resolved.

Content Updates and Fixes

• Issue #69: Fix typos and update several links in notebook «Advanced RAG on Hugging Face» - Typos and broken links can significantly affect user experience, so this issue should be addressed promptly.
• Issue #66: Finetuning Starcoder2 for python copilot - This issue involves adding a new article about finetuning a model, which could be valuable for users interested in customizing models for specific tasks. However, there's a question about its differentiation from existing content.
• Issue #64: [Draft] Add constrained generation cookbook - Constrained generation is an interesting topic that could enhance the project's value proposition. It's currently in draft status and requires review.

New Features and Use Cases

• Issue #65: Train - The title of this issue is not descriptive, which might lead to confusion. The content suggests an interesting project on creating a neural network for compiling a table of contents, but it lacks clarity and specifics.
• Issue #63: Effectively Annotate Text Data for Transformers via Active Learning using Cleanlab - This issue addresses an important aspect of machine learning workflows—data annotation. It has seen active discussion and revisions, indicating its complexity and significance.
• Issue #60: Update rag_llamaindex_librarian.ipynb - Ensuring that instructions are clear and complete is crucial for user success with tutorials. This issue aims to improve user guidance in one of the notebooks.

Miscellaneous

• Issue #57: RAG: Question Answering using Gemma, Elasticsearch & langchain - Collaboration with external entities like Elastic can bring valuable expertise and use cases to the project.
• Issue #48: Some outputs of code cells don't show up on hf.co/learn - This technical issue affects how content is displayed on the website, which could impact user experience.
• Issue #43: Comprehensive Cookbook for Fine-tuning Gemma Model on Mental Health Assistant Dataset - Ethical considerations are raised regarding the use case, highlighting the importance of responsible AI practices.

Recently Closed Issues

Noteworthy Closed Issues

• Issue #68: PR removes @MKhalusova as a default person to tag for a review - This change in review process could affect how future issues and PRs are handled.
• Issue #61: LLM-as-a-judge cookbook - A recently closed issue that added content on using LLMs as judges, which could be an intriguing application for users.

Summary

The open issues indicate active development in translation efforts (#70, #67, #34), content updates (#69), feature additions (#65, #63), and new use cases (#57). The project seems focused on improving user experience through better documentation (e.g., fixing typos in #69) and expanding its reach by translating content into multiple languages.

The closed issues suggest recent activity around refining the review process (#68) and adding significant new content (#61). The closure of these issues may indicate progress in streamlining contributions and expanding the project's scope.

Overall, the project appears to be in an active state of development with a focus on improving quality, expanding accessibility through translations, and exploring new features and applications.

Report On: Fetch pull requests

Analysis of Pull Requests in the huggingface/cookbook Repository

Open Pull Requests Overview

There are currently 9 open pull requests. Here's an analysis of some notable ones:

PR #70: Feat: Spanish Version

• Status: Draft, created 1 day ago.
• Summary: This PR aims to add a Spanish version of the cookbook, including translations of the table of contents and index. It is still a work in progress as not all pages have been translated.
• Notable Points:
  • It's a draft and not ready for final review.
  • The translation initiative is significant for non-English speakers.
  • The PR includes a large number of added lines (over 20k), indicating substantial content.

PR #69: Fix typos and update several links in notebook «Advanced RAG on Hugging Face»

• Status: Open, created 2 days ago.
• Summary: This PR addresses typos and updates links in an existing notebook. There was a discussion about the correct format for links when viewed on different platforms (VS Code vs. the Hugging Face website).
• Notable Points:
  • The PR seems to be a minor but valuable fix to improve the quality of documentation.
  • There's active communication between the contributor and reviewers to ensure accuracy.

PR #67: Update left articles Chinese version

• Status: Open, created 7 days ago, last edited 2 days ago.
• Summary: This PR updates several articles with their Chinese translations.
• Notable Points:
  • It's part of ongoing efforts to make the cookbook accessible to a wider audience by providing translations.

PR #66: Finetuning Starcoder2 for python copilot

• Status: Open, created 10 days ago.
• Summary: Adds a new article on how to fine-tune Starcoder2 for a Python copilot.
• Notable Points:
  • There is a question from a reviewer about how this notebook differs from an existing one on fine-tuning the StarCoder model.

PR #64: [Draft] Add constrained generation cookbook

• Status: Draft, created 14 days ago.
• Summary: Aims to add a cookbook on constrained generation.
• Notable Points:
  • Still in draft status and may need further refinement before it's ready for final review.

Recently Closed Pull Requests

PR #68: Removes @MKhalusova as a default person to tag for a review

• Status: Merged 4 days ago.
• Summary: A simple administrative change removing a default reviewer tag from the template.

PR #62: Minor Title Changes to the Cleanlab Notebook

• Status: Merged 22 days ago.
• Summary: Minor title changes for consistency across documentation.

PR #61: LLM-as-a-judge cookbook

• Status: Merged 15 days ago.
• Summary: Adds a prompting notebook detailing how to build LLM-as-a-judge and enforce constrained generation.

PR #60: Update rag_llamaindex_librarian.ipynb

• Status: Merged 24 days ago.
• Summary: A small change ensuring that users have pulled llama2 prior to running an example.

Summary

The open pull requests indicate active development and efforts to internationalize the content by adding translations in Spanish and Chinese. There are also contributions focused on improving existing notebooks by fixing typos or updating content. Most closed pull requests were merged, indicating that contributions are being actively reviewed and integrated into the project. A few pull requests were closed without merging, which may have been due to duplication of content or administrative changes that no longer needed to be made. Overall, there seems to be healthy activity in the repository with contributions being made across different areas such as translations, content updates, and new feature additions.

Report On: Fetch Files For Assessment

Analysis of the Source Code Structure and Quality

General Overview

The Hugging Face Cookbook repository is a community-driven project aimed at providing practical examples of building AI applications and solving various tasks with AI using open-source tools and models. The repository encourages contributions from everyone, emphasizing the importance of practical, clear, error-free notebooks that utilize open-source resources.

Specific Files Analysis

1. notebooks/en/llm_judge.ipynb

   • Purpose and Contribution: This Jupyter notebook is a recent addition to the cookbook, potentially introducing new techniques or models. Given the emphasis on practical examples in the repository, this notebook likely offers an end-to-end illustration of a specific AI development aspect or project.
   • Quality Assessment:
   • Practicality: Expected to align with the repository's goal of providing real-world applications. Should clearly explain objectives, challenges, and steps involved.
   • Clarity and Execution: Should be well-written, maintaining a friendly tone while being free from grammatical errors. It must execute without runtime errors to ensure usability by other community members.
   • Use of Open-Source Resources: Likely utilizes open-source libraries, datasets, and models, including links to all resources used within the notebook.
   • Contribution to Existing Recipes: As a new addition, it should offer unique insights or cover an area not previously addressed in the cookbook.

2. .github/pull_request_template.md

   • Purpose and Contribution: This file outlines the template for submitting pull requests (PRs) to the repository. The recent changes suggest updates to the project's contribution guidelines or PR process, aiming to streamline contributions and ensure consistency across submissions.
   • Quality Assessment:
   • Clarity and Guidance: Provides clear instructions for contributors on how to submit their PRs, including what information to include and how to structure their submission.
   • Encouragement for Contributions: The template encourages contributions by thanking potential contributors upfront and providing a mechanism for review follow-ups, fostering a welcoming community atmosphere.
   • Structured Review Process: By suggesting tagging members or contributors for review, it promotes a structured review process that can lead to higher quality contributions.

3. README.md

   • Purpose and Contribution: The README file serves as the front page of the repository, offering an overview of the project, its goals, contribution guidelines, and how to get involved. Recent edits likely reflect updates in project structure, goals, or contribution guidelines.
   • Quality Assessment:
   • Comprehensiveness: Provides a thorough introduction to the project's purpose, encouraging community contributions and detailing how individuals can contribute.
   • Clarity and Accessibility: Written in clear language that is accessible to newcomers. It outlines expectations for contributions (e.g., practicality, clarity, use of open-source tools) effectively.
   • Encouragement for Global Participation: The section on translating the cookbook into other languages demonstrates an inclusive approach towards global participation.

Summary

The structure and quality of these files indicate a well-organized project that values community contributions, clarity, inclusiveness, and practical utility in AI development. The detailed guidelines for contributions and PR submissions suggest a commitment to maintaining high-quality content that is accessible to a global audience. The addition of new notebooks like "llm_judge.ipynb" reflects ongoing efforts to expand the repository's scope with innovative AI techniques and models.

Report On: Fetch commits

Project Report: Hugging Face Cookbook

The Hugging Face Cookbook is an open-source project that provides community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models. The project is maintained by the organization Hugging Face, known for its contributions to the field of machine learning and natural language processing. The cookbook is a collection of Jupyter notebooks that illustrate end-to-end AI projects or specific aspects of AI development, emphasizing real-world applications, open-source tools, and clear documentation.

The project's overall state appears healthy and active, with a growing number of contributions from the community. It has garnered significant attention with 882 stars and 128 forks on GitHub, suggesting a high level of interest and engagement from developers and researchers. The repository contains a variety of resources, including guidelines for contributing and translating the content into different languages.

Team Members and Recent Activities

The following list details the recent activities of the development team in reverse chronological order:

• Maria Khalusova (MKhalusova)

  • Most recent commits involve removing mentions of herself as a default person to tag for reviews.
  • Collaborated on merging pull requests related to various topics like notebook improvements and contribution guidelines.
  • Patterns indicate she plays a role in managing pull requests and maintaining the repository's health.

• Aymeric Roucher (aymeric-roucher)

  • Contributed significantly to a notebook titled "LLM-as-a-judge" with multiple commits addressing issues, fixing errors, and improving content.
  • Engaged in updating titles, indices, and applying feedback to notebooks.
  • Demonstrates involvement in content creation and refinement.

• Aravind Putrevu (aravindputrevu)

  • Focused on minor title changes to the Cleanlab Notebook.
  • Merged updates from the main branch into his branch.
  • Appears to be involved in content updates and keeping notebooks up-to-date.

• Sara Han (sdiazlor)

  • Contributed to adding Argilla notebooks using Setfit and HF Inference Endpoints.
  • Addressed feedback on notebooks and made corrections.
  • Involved in content addition and responding to feedback for improvement.

• Richmond Alake (RichmondAlake)

  • Worked on filling information gaps in the MongoDB notebook.
  • Added database creation instructions, suggesting a focus on database integration with AI models.

• Pere Martra (peremartra)

  • Involved in implementing a semantical cache with FAISS in a RAG system.
  • Made corrections to text, instructions, and added explanations within notebooks.
  • Indicates an interest in retrieval-augmented generation systems and optimization.

• Jonathan Jin (jinnovation)

  • Added a notebook demonstrating RAG with LlamaIndex.
  • Focused on RAG-specific use cases and flexibility.
  • Shows an interest in retrieval-augmented generation systems.

Patterns observed from these activities suggest that the team members are actively collaborating on improving existing content, adding new examples/guides, addressing issues raised by users, and ensuring that the repository remains up-to-date. There is also a clear emphasis on maintaining high-quality documentation that is practical, clear, and error-free.

Conclusions

From the commit history and patterns observed, it is evident that the Hugging Face Cookbook project is under active development with contributions from multiple team members. The team is focused on expanding the cookbook's content while ensuring quality control through reviews and updates. The collaborative nature of the project is highlighted by the various merged pull requests from different contributors. This indicates a healthy open-source project environment where community contributions are encouraged and integrated into the main repository.

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
Maria Khalusova		1	1/1/0	1	2	8
		0	1/0/0	0	0	0
		0	1/0/0	0	0	0
		0	1/0/0	0	0	0
		0	1/0/0	0	0	0
		0	1/0/0	0	0	0

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}