Instructions to use adarshcod30/margadrishti-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use adarshcod30/margadrishti-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("adarshcod30/margadrishti-models", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 1,294 Bytes
55151a3 dc392dc 55151a3 dc392dc 55151a3 dc392dc 55151a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # Model Card Β· B4 β What caused this event, from the operator's free-text note?
*Generated 2026-09-02 from `reports/` β do not edit by hand.*
## What this model answers
What caused this event, from the operator's free-text note?
## Caveats
- Descriptions mix English and Kannada, often within one entry. Character n-grams are used rather than word tokenisation.
- Per-language F1 is reported because a single macro-F1 could hide a model that works on the English majority and fails on Kannada.
## Results
Ranked by **Macro F1 β** (higher is better). Every family that ran is listed, including those that lost β the comparison is the deliverable. Figures come from a single chronological split, so there is no spread to report.
| Model | Family | Macro F1 β | Accuracy β | Macro F1 Β· en β | Macro F1 Β· kn β | Fit | Beats baseline |
|---|---|---:|---:|---:|---:|---:|---|
| `tfidf_char_logreg` | nlp | 0.4782 | 0.6555 | 0.4833 | 0.4717 | β | yes |
| `majority` | baseline | 0.0441 | β | β | β | β | *baseline* |
**Best learned family: `tfidf_char_logreg`** at 0.4782, 10.8x the strongest baseline (`majority` at 0.0441).
## Reproduction
```bash
make data && make ingest && make features
make train-b
```
Seed `42`. Splits are chronological, never random.
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