| --- |
| license: cc0-1.0 |
| language: |
| - en |
| tags: |
| - competition |
| - baseline |
| - machine-learning |
| - kaggle |
| task_categories: |
| - other |
| pretty_name: Competition Baseline Kit |
| size_categories: n<1K |
| --- |
| |
| # Competition Baseline Kit |
|
|
| **CPU-only baselines for entering Hugging Face competitions in minutes — free to run.** |
|
|
| One command gets you a working submission on most HF competitions (generic CSV-submission |
| type). Replace the model with something better as the deadline allows; the plumbing stays. |
|
|
| ## How HF competitions work (two types) |
|
|
| 1. **Generic** — you get a training CSV + a test CSV, you upload a `predictions.csv`. |
| All test data is public. Baselines below cover this. |
| 2. **Script** — you submit code that generates predictions; test data can be hidden. |
| The kit's scripts adapt (write your training inside a `train()` and save the model). |
|
|
| ## Quick start |
|
|
| ```bash |
| # text classification/regression (e.g. movie-genre-prediction style) |
| python baseline_text.py --train train.csv --test test.csv --target label --text-col text |
| |
| # tabular (e.g. wyze-rule-recommendation style) |
| python baseline_tabular.py --train train.csv --test test.csv --target label |
| ``` |
|
|
| Output: `submissions/predictions.csv` — exactly what the competition UI asks you to upload. |
|
|
| ## Kit contents |
|
|
| | File | Use | |
| |---|---| |
| | `baseline_text.py` | TF-IDF (1-2 grams) + LogisticRegression — strong text baseline | |
| | `baseline_tabular.py` | HistGradientBoosting (classifier/regressor auto-detect) — handles NaN + categories | |
|
|
| ## The winning workflow (this is the actual edge) |
|
|
| 1. **Monitor daily** — new competitions get announced on the [HF forum (Community Calls)](https://discuss.huggingface.co/c/community-calls) and the [competitions org](https://huggingface.co/competitions). Being first means more submissions and more learning. |
| 2. **Enter within 24h** of launch with a baseline — you now have a leaderboard position and can iterate publicly. |
| 3. **Iterate free**: feature engineering, ensembling, and (if the task needs it) small fine-tunes on CPU/Colab-free resources. |
| 4. **Publish your solution** on the Hub after the deadline — it becomes portfolio material (models, datasets, write-ups) that feeds the consulting side of the profile. |
|
|
| ## Known active/live reference |
|
|
| - KDD Cup 2026 — Tencent UNI-REC ($885k pool) and HKUST Data Agents ($30k pool) announced March 2026: https://dataagent.top/ |
| - ICML 2026 agent reproduction challenge (closed Aug 2, 2026) — pattern to watch for 2027 |
|
|
| ## License |
|
|
| CC0 — steal the baselines, they're meant to be forked. |
|
|
