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RubixML/Sentiment

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An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

active 2025-01-01 → 2025-08-10 (UTC)

Partial coverage12,935 / 15,151 hourly files (85%) · 2 absent upstream · 2,210 failed, retryable2024-11-17 → 2026-08-11 (UTC)— sampled evenly across the window, so rankings and trends hold; absolute counts scale up.
Events
7
Pushes
1
Pull requests
0
Issues
1
Stars
4
Forks
1

Activity over time

Daily event counts in the loaded window

Line chart, 222 days from 2025-01-01 to 2025-08-10. Pushes: 1 total, peak 1 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 1 total, peak 1 in a day. Comments: 0 total, peak 0 in a day. Stars: 4 total, peak 1 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
Xoshbin1000
andrewdalpino1100

Recent activity

Latest issues, pull requests and releases

  • Issue#7Xoshbin2025-01-01 20:00
    Is there anyway to report back if the prediction was wrong or right

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 4 stars here means stars gained during the window, not the repo's star count.