Classify rows in a CSV
Run offline batch classification over a CSV file — label each row with a category, sentiment, or custom class using an open model, with results written back as a labeled CSV.
Estimated $0.001–$0.01 per row, depending on model size · Scales with row count — roughly 500–2,000 rows/min
What inputs you need
- CSV file with a text column to classify
- Label set / classification prompt
- Model choice
What Badgr returns
- Labeled CSV (original rows + predicted label + confidence)
- Batch run log
- Single receipt on completion
Recommended GPU routes
Estimated cost: $0.001–$0.01 per row, depending on model size · Estimated runtime: Scales with row count — roughly 500–2,000 rows/min
Example command
badgr run "python batch_classify.py --input rows.csv --labels labels.json" \
--gpu A100_40GB \
--max-cost 10 \
--max-runtime 60Common setup failures Badgr avoids
Job stops partway through a large CSV — input is chunked and progress checkpointed so a restart resumes instead of reprocessing.
Max-cost hit before the full file completes — estimate cost per row from a small sample run before submitting the full CSV.
Ready to run this?
Launch from the dashboard, CLI, or Compute API. Max-cost protection included.