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What you are actually evaluating

The phrase “Kannada model” can describe several systems: a Kannada speech interface for farmers, a language model that extracts information from invoices, a translation model for buyer–weaver communication, or a multimodal system that assesses cocoon or silk quality from images. These systems should not be judged only by generic Kannada benchmarks. They must be tested against the decisions, documents, dialects, and operating conditions found in Karnataka’s silk ecosystem.

A useful evaluation asks four questions:

• Does the model understand the user and the business context?
• Does it produce an accurate, verifiable output?
• Can workers use it reliably in real conditions?
• Is the benefit large enough to justify deployment and maintenance?

Before testing, define the exact task, users, languages, devices, and acceptable failure modes. A chatbot that answers scheme-related questions has different requirements from a model that extracts lot numbers or recommends a transport route.

Map the supply-chain tasks and risks

Break the workflow into stages rather than evaluating one broad “silk model.” Typical use cases include:

• Sericulture: recording mulberry cultivation, silkworm batch details, disease symptoms, feeding schedules, and harvest dates.
• Procurement: capturing farmer declarations, weighing records, auction information, prices, and quality grades.
• Reeling and processing: tracking denier, breaks, moisture, defects, machine downtime, and batch traceability.
• Weaving: managing designs, yarn inventories, production plans, and artisan instructions.
• Sales and logistics: translating buyer requirements, generating invoices, forecasting demand, and tracking dispatches.

Assign a risk level to each task. A minor spelling error in a product description is not equivalent to a wrong disease warning or a misread quality grade. High-risk outputs should require human approval, citations to source records, and an audit trail.

For interfaces involving text, speech, or images, document the input conditions: Kannada script, mixed Kannada-English messages, regional vocabulary, code-switching, low-bandwidth uploads, noisy audio, and photographs taken under inconsistent lighting. If the model will support multiple Indian languages, a practical guide to open-source vision-language models for Indian languages can help frame image, text, and language coverage decisions.

Build a representative Kannada evaluation set

A strong test set is more valuable than a large but generic corpus. Collect de-identified examples from the actual workflow, with consent and clear data governance. Include:

• Farmer and artisan questions written in formal Kannada, colloquial Kannada, and Kannada-English mixtures.
• Regional terms for cocoons, diseases, tools, yarn defects, measurements, units, and payment practices.
• Scanned forms, handwritten registers, invoices, auction slips, WhatsApp messages, and voice notes.
• Product names, village names, personal names, abbreviations, numerals, dates, and rupee amounts.
• Difficult audio recorded on farms, markets, workshops, and during machinery operation.
• Counterexamples where the correct answer is “insufficient information” or “refer to an expert.”

Create a gold-standard label for every example. At least two trained annotators should independently label outputs such as intent, entities, translation, grade, or answer correctness. Resolve disagreements with an adjudicator and retain the disagreement record; it reveals where the model and the business process are both ambiguous.

Do not place sensitive phone numbers, bank details, Aadhaar information, or identifiable farmer records in public repositories. Maintain separate development, validation, and locked test sets so that prompts and fine-tuning do not leak evaluation examples. Teams publishing reusable code can borrow practices from building computer vision models on GitHub, especially dataset versioning, reproducible experiments, and documented evaluation scripts.

Measure task accuracy, not just fluent Kannada

Use metrics suited to each task:

• Classification: macro-F1, per-class recall, and confusion matrices. Macro-F1 prevents common intents from hiding poor performance on rare but important cases.
• Entity extraction: exact-match and span-level precision, recall, and F1 for names, quantities, dates, grades, and lot identifiers.
• Speech recognition: Kannada word error rate, character error rate, and separate results for noisy audio, accents, and code-switching.
• Translation: human adequacy and terminology accuracy alongside automated scores. A fluent sentence that changes a measurement or treatment instruction is a serious failure.
• Document extraction: field-level accuracy, numeric exactness, table integrity, and abstention rate on unreadable documents.
• Question answering: factual correctness, source-groundedness, completeness, and refusal quality when evidence is missing.
• Image analysis: precision and recall by defect or quality class, calibration, and performance across lighting, camera, and background conditions.

Report confidence intervals, sample counts, and results by subgroup. A single overall score can conceal weak performance for low-frequency dialects, women’s voices, rural recordings, handwritten documents, or small producer groups.

For model comparisons, keep prompts, decoding settings, retrieval sources, and test data fixed. If a model is used through an API, record model version, latency, token or image cost, and outage behaviour. For local deployment, measure memory use, throughput, battery impact, and performance on the actual device. Teams comparing language systems can also consult methods used in benchmarking NLP models for Telugu and Sanskrit, while adapting the protocol to Kannada and silk-specific terminology.

Add human and operational evaluation

A model can score well and still fail in a market or workshop. Run a supervised pilot with farmers, procurement staff, reelers, weavers, supervisors, and buyers. Give participants realistic tasks and measure:

• Completion rate and time saved compared with the current process.
• Number of corrections, re-entries, escalations, and abandoned interactions.
• Whether users understand confidence indicators and know when to verify an answer.
• Accessibility on low-end Android phones and intermittent networks.
• Training time, perceived usefulness, and willingness to use the system again.
• Impact on payment disputes, stock errors, missed dispatches, or quality rejections.

Use a control or baseline wherever possible. Compare the AI workflow with existing registers, spreadsheets, or human translation—not with an idealised process. Track financial outcomes such as reduced rework, faster reconciliation, lower travel or support costs, and fewer rejected lots. Include recurring expenses for hosting, annotation, support, security, and model updates when calculating return on investment.

Test safety, fairness, and traceability

For production use, evaluation must cover more than accuracy. Test whether the system:

• Protects farmer, worker, buyer, and payment information.
• Distinguishes verified records from generated suggestions.
• Avoids inventing prices, government benefits, disease advice, or delivery commitments.
• Handles adversarial prompts, malformed files, prompt injection, and misleading documents.
• Produces logs that allow a supervisor to reconstruct the input, model version, output, and correction.
• Escalates uncertain or high-impact cases to a named human role.

Measure subgroup gaps across geography, gender, age, dialect, literacy, device type, and connection quality. Ask annotators to review harmful stereotypes, disrespectful phrasing, and failures involving caste, occupation, or community identity. A multilingual system should support informed consent and clear Kannada explanations of what data is collected and why.

If the workflow includes images of cocoons, yarn, looms, or defects, document image ownership and consent. For teams deploying models themselves, deploying large language models locally may reduce data-exposure risk, but local deployment still requires patching, access control, monitoring, and hardware planning.

A practical 90-day evaluation plan

Weeks 1–2: Scope. Select one high-value, bounded task; define users, labels, risk controls, baseline, and success thresholds.

Weeks 3–5: Data. Gather representative Kannada text, speech, documents, or images. Annotate, adjudicate, anonymise, and version the dataset.

Weeks 6–7: Offline testing. Compare candidate models using fixed settings. Report task metrics, subgroup results, cost, latency, and failure examples.

Weeks 8–10: Field pilot. Run the system with trained users under supervision. Record corrections, abstentions, connectivity problems, and operational outcomes.

Weeks 11–12: Decision. Deploy only if quality, safety, usability, and unit economics meet predefined thresholds. Otherwise, narrow the task, improve data, add retrieval or human review, and repeat the test.

The result should be a model card or evaluation report containing the intended use, excluded use cases, data provenance, Kannada coverage, metrics, known failures, privacy controls, operating cost, and escalation process. This turns evaluation into a repeatable management tool rather than a one-time demo—and gives Karnataka’s silk organisations a defensible basis for choosing, improving, or rejecting an AI system.