Community Notes Data Loading and Preprocessing Pipeline

[praneethreddy31]

This module is the backbone of the Community Notes scoring algorithm's data pipeline. It is responsible for the critical first steps of ingesting, cleaning, and preparing the raw data for analysis. Its core functions include:

Data Ingestion: Securely reading and parsing raw TSV files for notes, ratings, note status history, and user enrollment into pandas DataFrames.

Data Cleaning: Handling duplicates, filtering out irrelevant or outdated entries (such as ratings for non-misleading notes), and ensuring data integrity.

Feature Engineering: Creating essential columns used in scoring, such as the unified helpfulNumKey, and identifying high-volume raters.

Data Filtering: Applying core filtering logic based on the minimum number of ratings per note and per rater to create a viable dataset for matrix factorization.

Serialization: Providing utilities to safely write processed data and model outputs to disk, using secure formats like JSON for metadata and TSV for tabular data.

Data Loading Abstraction: Defining the CommunityNotesDataLoader classes which abstract the data source, allowing the scoring algorithm to run seamlessly on either local files or production data sources.

This module ensures that the data fed into the scoring models is clean, consistent, and structured correctly, which is essential for the accuracy and reliability of the entire Community Notes system.

[CLAassistant]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.

---

Praneeth seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.
_{You have signed the CLA already but the status is still pending? Let us recheck it.}