Fine-tuning MuRIL for Multilingual Citizen Grievance Classification (with all the bugs I hit)
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Built a text classifier that routes Indian citizen grievances (Hindi / Hinglish / English, code-mixed) into 14 government department categories , using a fine-tuned google/muril-base-cased. Final honest holdout metrics: 87.3% accuracy, 0.869 macro F1. Full writeup of the bugs I hit below, because the debugging was more instructive than the happy path. Dataset: https://www.kaggle.com/datasets/abhisheksingh016/citizen-grievance-dataset Notebook:…
1Key Takeaways
- Built a text classifier that routes Indian citizen grievances (Hindi / Hinglish / English, code-mixed) into 14 government department categories , using a fine-tuned google/muril-base-cased.
- Final honest holdout metrics: 87.3% accuracy, 0.869 macro F1.
- Full writeup of the bugs I hit below, because the debugging was more instructive than the happy path.
- Dataset: https://www.kaggle.com/datasets/abhisheksingh016/citizen-grievance-dataset Notebook:….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that built a text classifier that routes Indian citizen grievances (Hindi / Hinglish / English, code-mixed) into 14 government department categories , using a fine-tuned google/muril-base-cased.
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