In this project, we explore text classification by combining word embeddings generated using Gensim and supervised learning models. Word embeddings are dense vector representations of words that capture semantic relationships between them, allowing machine learning models to understand text in a more meaningful way than simple one-hot encodings.
active 2024-09-11 → 2024-09-11 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-09-11 to 2024-09-11. Pushes: 3 total, peak 3 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 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
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| 1216-dev | 3 | 3 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.