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| Source | Type | Rows |
|---|---|---|
| engreemali/bank-transactions-sms-datasetss | Real Indian SMS (cleaned) | ~1,200 |
| kumarperiya/pan-indian-consumer-transaction-dataset | Structured → synthetic SMS | ~600 |
| ChatGPT synthetic_sms_5000 (fixed) | Synthetic (augmented) | ~3,300 |
| ChatGPT realistic_synthetic_sms (fixed) | Synthetic (realistic) | ~3,200 |
| ID | Category |
|---|---|
| 0 | Education |
| 1 | Entertainment |
| 2 | Food |
| 3 | Healthcare |
| 4 | Shopping |
| 5 | Transport |
| 6 | Utilities |
1from transformers import pipeline
2clf = pipeline('text-classification', model='udayugale/expense-tracker-distilbert-lora-v2')
3print(clf('Netmeds medicine order rs 350 confirmed. Delivery in 2 hrs'))
4# [{'label': 'Healthcare', 'score': 0.95}]Income and Others labels (not in expense categories)Bills → Utilitiessender column from File 2 (2,376 sender-label mismatches)